<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
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<meta name="renderer" content="webkit">
<title>【维普论文检测系统-大学生版】比对报告</title>
<style type="text/css">
.layout_left,.layout_right{
scrollbar-shadow-color:#d7d7d7;
scrollbar-highlight-color:#d7d7d7;
scrollbar-3dlight-color:#d7d7d7;
scrollbar-darkshadow-color:#d7d7d7;
scrollbar-arrow-color:#848484;
scrollbar-face-color:#848484;
scrollbar-track-color:#d7d7d7;
overflow-y:scroll;
}

* html,* html body{background-image:url(about:blank);_background-attachment:fixed} 
body, div, dl, dt, dd, ul, ol, li, h1, h2, h3, h4, h5, h6,pre, form, fieldset, input, textarea, p, blockquote, th, td {padding: 0; margin:0;}
body{padding:0 0 30px 0;background:url("../images/bodybg.gif") repeat;_background-attachment: fixed;/* prevent screen flash in IE6 确保滚动条滚动时，元素不闪动*/font-size:12px;font-family:'宋体';color:#252525;word-wrap:break-word;word-break:break-all;}
fieldset, img {border:0;vertical-align:middle;}
table {border-collapse: collapse; border-spacing: 0;}
ol, ul {list-style: none;}
address, caption, cite,em, strong, th {font-weight: normal; font-style: normal;}
th,td {text-align: left;border: 1px solid;}
i,b,strong,em{font-style:normal;}
input,select{vertical-align:middle;}

a{color:#0e53b0;text-decoration:none;}
a:visited{text-decoration:none;}

/*################字体样式######################*/
.red{color:#ff3000;}
.blue{color:#0061c5;}
.self{color:#107400;}
.gray{color:#666;}
m{display:block!important;background: #f3f3f3;padding:14px;margin: -50px -14px -14px -14px;border: 1px solid #eaeaea;}
.yellow{color:#e0ae00}
.black{color:#252525;}
.white{color:white;}
.bold{font-weight:bold;}
.normal{font-weight:normal;}
.f28{font-size:28px;}
.f26{font-size:26px;}
.f24{font-size:24px;}
.f22{font-size:22px;}
.f20{font-size:20px;}
.f18{font-size:18px;}
.f16{font-size:16px;}
.f14{font-size:14px;}
.f13{font-size:13px;}
.f12{font-size:12px;}
.f11{font-size:11px;}
.f10{font-size:10px;}
.f9{font-size:9px;}
.f8{font-size:8px;}
.tahoma{font-family:"Tahoma";}
.song{font-family:"宋体";}
.heiti{font-family:"黑体";}
.yahei{font-family:"微软雅黑";}
.verdana{font-family:"Verdana";}
.a_left{text-align:left;}
.a_right{text-align:right;}
.a_center{text-align:center;}
.block{display:block;}
.float_left{float:left;}
.float_right{float:right;}
.dec_none{text-decoration:none;}
.dec_line{text-decoration:underline;}

.input_400{width:380px;height:35px;background:url("../images/inputbg_400.gif") no-repeat;border:none;line-height:35px;padding:0 10px;}
.btn_upfile{width:113px;height:35px;background:url("../images/btn_upfile.gif") no-repeat;border:none;cursor:pointer;}
.btn_upfile02{width:113px;height:35px;background:url("../images/btn_upfile02.gif") no-repeat;border:none;cursor:pointer;}
.btn_120{width:120px;height:35px;background:url("../images/btn_120.gif") no-repeat;border:none;cursor:pointer;line-height:35px;}

.topbar{height:70px;background:#e2e2e2;position:relative;overflow:hidden;border-bottom:2px solid #545454}
.loginbar{height:35px;position:absolute;right:20px;top:0;}
.loginbar li{display:block;float:left;height:35px;line-height:35px;padding:0 10px;background:url("../images/topline.gif") no-repeat 0 center;}
.loginbar li a{display:block;height:35px;float:left;line-height:35px;color:#275075;text-decoration:none;}
.loginbar li a:hover{color:red;}

 
.layout_body{height:100%;overflow:hidden;background:none;}
.layout_left{width:70%;height:1000px;float:right;overflow:auto;position:relative;}
.layout_right{width:100%;height:1000px;margin-left:-70%;float:right;background:#e2e2e2;overflow:auto;}
.content{margin:0 0 0 70%;color:#171717;}
.artical{width:auto;background:#fafafa;border:1px solid #fafafa;margin:0px auto;line-height:2em;position:relative;}
.tit{padding:0 0 20px 0;line-height:3em;}
.detail{padding:20px 50px;}
.tit_detail{margin:10px 0 20px 0;}
.detail h1{line-height:3em;color:#222;}
.reportinfo{background:#fafafa;border-bottom:2px dotted #ddd;padding:10px 20px;margin:auto;line-height:2.6em;color:gray}


.sidebars{padding:10px 30px;line-height:2.2em;border-bottom:1px dashed #aaa;border-top:1px dashed #efefef;}
.sidebars p{display:block;padding:5px;}



.reportnav{position:absolute;left:420px;top:20px;}
.reportnav li{display:block;float:left;margin-right:6px;}
.reportnav a{display:block;padding:10px 15px;background:#e9e9e9;border:1px solid #888;color:black;text-decoration:none;font-size:12px;font-weight:bold;}
.reportnav a:hover{background:#f6dfdb;color:#ff2a00;border:1px solid #b30000}
.reportnav-link{background:#f6dfdb !important;color:#ff2a00 !important;border:1px solid #b30000 !important}

/*整体报告部分*/
.layout_all{overflow:auto;position:relative;background:#444;}
.all{width:800px;background:#fefcf1 url("../images/stamp.gif") no-repeat;border:3px solid black;padding:20px;margin:50px auto;line-height:2em;position:relative;}
.ztbg{width:90%;margin:30px auto;border:1px solid #888;}
.ztbg th,.ztbg td{padding:5px 10px;border:1px solid #bbb;}
.ztbg th{width:20%;background:#f7f7f7;}
.ztbg .tablebg{background:url("../images/tablebg.gif") repeat-x;}


.corp{width:95%;margin:0 auto;padding:20px;text-align:center;line-height:2em;color:#888}
.corp a{color:#888;}
.corp a:hover{color:#888;}
/*修改后的样式&&新增样式*/
.layout_right{background-color: #FFF;}
.maodian{border-bottom: 2px solid #e0e0e0;min-height: 69px;overflow: auto;border-bottom: 0px;width: 570px;background-color: #fff;box-sizing: border-box;}
.maodian a{float: left;width: 24px;height: 24px;background-color: #ededed;color: #a2a3a3;font-family: arial;text-align: center;text-decoration: none;margin: 0 3px 3px 0;}
.maodian .point{background-color: #2672f2;color: #FFF;}
.repeat,.xiangsi{font-family: "微软雅黑";font-weight: 100;color: #666;}
.tag{padding: 0;width: 100%;height: 34px;background-image: url();background-repeat: no-repeat;border: 0;margin-bottom: 20px;}
.sidebars{border-top: 0;}
.tag a{float: left;width: 48px;height: 25px;text-decoration: none;color: #fff;font-size: 14px;font-weight: bold;font-family: arial;margin-top: 2px;text-align: center;cursor: pointer;}
.fix{position: fixed;top: 0;width: 565px;}
.content{position: relative;height: 565px;}
.content-fixed{overflow: hidden;}
.content-fixed1{overflow: auto;}
#report_part_2>div:nth-child(2){/*width:90%;*/padding: 0;margin: 0 auto;}
#report_part_2>div:nth-child(3){border-bottom: 0;line-height: 18px;}
.block img{margin-right: 5px;}
.sidebars ul{line-height: 18px;}
.more-btn{display: block;width: 28px;height: 12px;background-color: #f1f1f1;border-radius: 2px;text-align: center;line-height: 6px;color: #999;font-weight: bold;}
.more-btn:hover{color: #999;text-decoration: none;}
.more-btn-active:hover{color: #f1f1f1;}
.hide ul{display: none;}

i{background:blue;}

.vpcs-tips{
	background:#9c9c9c;
	color:#fff;
	padding:0 5px;
	margin-right:10px;
	height:20px;
	line-height:18px;
	display:inline-block;
	}

.vpcs-color{
	width:10px;
	height:10px;
	display:inline-block;
	margin-right:5px;
	}
	
.vpcs-color-block{
	background:#000;
	}
	
.vpcs-color-red{
	background:#F00;
	}
	
.vpcs-color-yellow{
	background:#d27303;
	}
	
.vpcs-color-green{
	background:#107400;
	}
	
.vpcs-color-blue{
	background:#00f;
	}
	
.vpcs-footer{
	width:100%;
	background:#eee;
	padding:0 10px;
	padding-top:10px;
	margin-top:10px;
	font-size:12px;
	height:150px;
	border-top:1px solid #000;
	}
	
.vpcs-footer-div{
	height:24px;
	line-height:24px;
	}
	
.vpcs-footer-title{
	font-weight:bold;
	}
	
.vpcs-footer-line{
	padding-bottom:10px;
	border-bottom:1px dashed #000;
	}
	
.vpcs-footer-top{
	margin-top:2px;
	}
	
.vpcs-footer a:hover{
	color:#F00;
	background:none;
	}
	
.vpcs-footer-left{
	float:left;
	min-width:600px;
	/*width:calc(100% - 101px);*/
	width:80%;
	}

.vpcs-footer-right{
	float:right;
	width:90px;
	height:150px;
	}
	
.vpcs-footer-right > img{
	width:90px;
	height:90px;
	}
	
.vpcs-footer-right > div{
	width:100%;
	height:20px !important;
	text-align:center;
	}
	
.vpcs-blue:link,.vpcs-blue:visited{
	color:#00f;
	}
	
.vpcs-blue:hover{
	background:#00f !important;
	color:#fff !important;
	}

	
.vpcs-green:link,.vpcs-green:visited{
	color:#107400;
	}
	
.vpcs-green:hover{
	background:#107400 !important;
	color:#fff !important;
	}

	
.vpcs-yellow:link,.vpcs-yellow:visited{
	color:#d27303;
	}
	
.vpcs-yellow:hover{
	background:#d27303 !important;
	color:#fff !important;
	}

	
.vpcs-red:link,.vpcs-red:visited{
	color:#F00;
	}
	
.vpcs-red:hover{
	background:#F00 !important;
	color:#fff !important;
	}
	/******新增样式*********/
	
.cqvip-colorstyle{ padding:4%; border-bottom:1px solid #747474;}
.bdbgsjlwpd{ padding:10px 0; border-bottom:1px dashed #ccc;}
.bdbgxsjz{ padding:10px 0;}
.cqvip-colorstyle-1{ background:#f4f4f4;}
.cqvip-colorstyle-2{ background:#fff;}
.bdbgtitles{ width:100%; height:35px; margin-bottom:10px;}
.bdbgtitles-icon{ width:48px; height:30px; display:inline-block; text-align:center; line-height:25px; color:#fff;}
.bdbggray{ color:#6f747a; padding:0;}
</style>
<style type="text/css">
body{background:#e2e2e2;padding:0;}
table{width:100%}
td{padding:2px;}
.imgdiv{width:100%;text-align:center;margin:10px;}
.imgdiv img{border:1px solid gray;max-width:800px;}
.selectedA{background:red;color:#fff;}
em{color:red;}
i{background:blue;}
.maodian-content{position: fixed;top: 0;background-color: #fff;border-top: 1px #F1F1F1 solid;max-height: 200px;width: 30%;overflow-y:scroll;overflow-x:hidden;}
.h{background:#cc1d1d;color:white;}
.s{background:#cc1d1d;color:white;}
.k{background:red;color:white;}
.squre{ width:10px; height:10px; display:inline-block; margin-right:10px;margin-top:2px;}
.bdbg-bjcolor{ background:rgba(235,235,235,1); border-bottom:1px solid #747474;}

/********返回顶部样式**********************/
.jcbg-fhdb{ width:39px; height:99px; position:fixed; bottom:50px; left:50%; margin-left:430px; font-size:12px;}
.jcbg-fhdb-img{ position:absolute; left:0; top:0;}
.jcbg-sqbqdj{ position:absolute; color:#2477b1 !important; z-index:9; text-decoration:none !important; left:4px; top:8px; color:; display:block; width:31px; text-align:center; line-height:18px;}
.jcbg-sqbqdj:hover{ color:#175f91 !important;}
.jcbg-fhdb-top{ position:absolute; left:0; bottom:16px; height:12px; z-index:10; cursor:pointer; font-size:14px; width:39px; text-align:center; font-weight:bold; color:#929292;}
.laiyuan_songjian{display:inline-block;width:16px;height:16px;background-image:url();background-repeat:no-repeat;background-position:0 0;vertical-align:middle;}
.laiyuan_fanhui{display:inline-block;width:16px;height:16px;background-image:url();background-repeat:no-repeat;background-position:0 0;vertical-align:middle;}
</style>
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个细胞单元，而最终的特征向量是由与区间大小一样的窗口\n滑动产生","copyCount":1.0,"copyKeywords":"疲劳检测;眼状态识别;HOG;特征;灰度形态学","startIndex":20573.0,"endIndex":20612.0,"checkid":1.33210938E8,"isQueto":0.0,"md5Code":"6b0242d16b2577c83e5ec1ab636aeb87"},{"id":3.2382719E7,"fileName":"2019052108283143859751.doc","checkContent":"像素*2像素大小，每个块采用8像素*8像素大小，也就是每个块包含了2*2大小的细胞单元。\n将\u003cbr/\u003e块，每个块的大小就是16x16\n像素。在每个细胞单元中计算梯度方向直方图时，要将","returnContent":"中的HOG特征提取都是在大小为64 (列）xl28 (彳丁）的图像上进行的，\n首先要将图像划分成小的连通区域，也就是细胞单元。细胞单元规定为8x8像素\n大小，一个训练样本图像可以划分成8x16 \u003d 128个细胞单元，计算细胞单元中每个\n像素的梯度方向直方图，将所有的梯度方向直方图联结起来就形成了一个特征描\n述器。为了提高效率，可以将每相邻的四个细胞单元组合成一个块（block)，块\n的形成是根据每个细胞单元进行滑动生成的，块每次滑动八个像素也就是一个细\n胞单元的长度。一个训练样本可以得到7x15 \u003d 105个块，每个块的大小就是16x16\n像素。在每个细胞单元中计算梯度方向直方图时，要将各像素点的梯度方向进行\n投影，投影在九个区间一共是180°也，就是每20°―个区间。将每个块中的四个细\n胞单元的梯度方向直方图联结起来就构成了块的特征描述器，每个块描述器都是\n一个36维的向量。最后将所有的块描述器依次联结起来，这样就构成了一个训练\n样本的特征描述器，每一个训练样本特征描述器的大小就是36x105 \u003d 3780维。如\n图2.4所示。\n...i；...设. 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105个\u003cem\u003e块，每个块的大小就是16x16\n像素。在每个细胞单元中计算梯度方向直方图时，要将\u003c/em\u003e各像素点的梯度方向进行\n投影，投影在九个区间一共是180°也，就是每20°―个区间。将每个块","copyCount":2.0,"copyKeywords":"人体检测;HOG特征;部件模型;级联检测","startIndex":20575.0,"endIndex":20628.0,"checkid":1.33210938E8,"isQueto":0.0,"md5Code":"6b0242d16b2577c83e5ec1ab636aeb87"}],"checkContent":"每个细胞单元采用2像素*2像素大小，每个块采用8像素*8像素大小，也就是每个块包含了2*2大小的细胞单元。\n将","endIndex":20628.0,"checkContentShow":"度直方图的图像表示\n我们采用的样本集是20像素*20像素的图片，\u003cem\u003e每个细胞单元采用2像素*2像素大小，每个块采用8像素*8像素大小，也就是每个块包含了2*2大小的细胞单元。\n将\u003c/em\u003e正负样本图片的路径传入方法，得到正负样本的HOG特征。实现代码如","chapterNum":0.0,"index":42},"19092":{"num":40.0,"wordNum":50.0,"startIndex":19092.0,"color":"red","copyRate":77.0084,"yyIndexs":[],"list":[{"id":3.2382713E7,"fileName":"2019052108283143859751.doc","checkContent":"正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为-1）。获取图片\u003cbr/\u003e正样本图片和 12180 张负样本图片，图片大小均为 × \n像素。将正样本图片类别标记为+1，负样本","returnContent":"默认值为 1；\n上述参数的设置可按照 SVM 类型和核函数类型进行任意组合，如果参数在核函\n数中不会用到，则程序不接受该参数，如果参数设置错误，则程序采用参数的默认\n值。\n 表示用于训练的数据集，数据格式需满足LIBSVM要求， \n为训练后产生的模型文件，文件中包含支持向量的样本数，支持向量样本，以及拉\n格朗日系数等参数。\n5.2.2 训练流程\n（1）线性 SVM 分类器训练\n行人检测本质上属于二分类过程，即判断待检测窗口是否包含行人，本文采用\nLIBSVM 软件包中的\u0017 函数进行分类器训练。\n对于线性 SVM 分类器，分类器的训练分为两个步骤：\n首先是获取用于训练的正负样本图片，生成训练数据集。INRIA 数据集中包含了\n2416 张正样本图像和 1218 张负样本图像，其中正样本图像大小为 × 像素，根\n据数据集提供的行人标注文件，可从正样本图像中提取出 × 像素大小的图片\n作为正样本，再从每张负样本图像中随机选取 10 张 × 像素大小图片作为负样\n本，这样一共获得 2416 张正样本图片和 12180 张负样本图片，图片大小均为 × \n像素。将正样本图片类别标记为+1，负样本图片类别标记为-1，对每幅正、负样本\n图片分别提取 HOG 特征，生成 LIBSVM 数据格式的文本文件，作为训练数据集；\n其次是分类器的训练过程，将训练数据集输入\u0017 函数，SVM 类型值和核函\n数类型值均设为 0，训练得到初始的线性 SVM 分类器，再使用初始分类器对原始的负\n样本图像进行检测，将分类错误的窗口区域标记为困难样本(\f )，把困难\n样本添加到初始负样本集中，类别标示为-1，分别提取每张正样本图片、负样本图\n片及困难样本图片的 HOG 特征，生成训练数据集，对分类器进行二次训练，","keyWordsShow":"\u003cem\u003e过访问路径下的正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为-1）。获取图片\u003c/em\u003e","title":"复杂动态背景下基于近似IKSVM分类器的行人检测","author":"葛金炬","comeFrom":"博硕","percentage":50.063,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"06","organ":"上海交通大学","textid":"be6cc42de60b4356834f8ddd1d81f1d0","category":"CMFD","context":"本，再从每张负样本图像中随机选取 10 张 × 像素大小图片作为负样\n本，这样一共获得 2416 张\u003cem\u003e正样本图片和 12180 张负样本图片，图片大小均为 × \n像素。将正样本图片类别标记为+1，负样本\u003c/em\u003e图片类别标记为-1，对每幅正、负样本\n图片分别提取 HOG 特征，生成 LIBSVM 数据格","copyCount":1.0,"copyKeywords":"路面提取;ROI尺度归一;行人检测;HOG特征描述子;IKSVM","startIndex":19092.0,"endIndex":19142.0,"checkid":1.33210894E8,"isQueto":0.0,"md5Code":"a82d9d6ee4670f91dc2c7a5f10878b6f"},{"id":3.2382714E7,"fileName":"2019052108283143859751.doc","checkContent":"正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为\u003cbr/\u003e正样本和负样本的 HOG 特征，并对样本进行标记，例如正\n样本标记为 1，负样本标记为","returnContent":"M 的训练步骤\n输入：正样本和负样本，大小均为 32×64 像素\n输出：基于窗口大小为 32×64 像素的二类分类器\n1. 采用初始样本集，进行第一阶段的训练：\n1)分别计算正样本和负样本的 HOG 特征，并对样本进行标记，例如正\n样本标记为 1，负样本标记为-1；\n2)创建 SVM 对象，并设置 SVM 类型、核函数的类型以及 SVM 参数的\n类型；\n3)把所有样本的 HOG 特征放到 SVM 中进行训练；\n4)把得到的分类器参数存入文本文件中，参数的个数为(HOG 特征维数\n+1)。\n2. 提取困难样本。为了检测出负样本中被错误分类的窗口，需要多次对图\n片进行缩放，在每一次缩放的图像中进行特征扫描：\n1)令初始尺度 Ss\u003d 1，结束尺度 Se\u003d min(Wi/32, Hi/64)，其中 Wi和 Hi表\n示输入图片的原始宽度和高度；\n2)假设 Sr为尺度变化步长，则需要使用的尺度集合 S \u003d (Ss, Ss×Sr,Ss×Sr2,\nSs×Sr3,… ,Se)；\n3)对于第 i 张输入负样本图片，\na) 按照当前尺度缩放（一般是缩小）原始图片；\nb) 使用 32×64 的检测窗口对缩放后的图片从上到下，从左到右，\n以固定步长（例如 4 个像素）进行密集扫描，提取 HOG 特征，\n以步骤 1-4)得到的分类器参数进行运算，得出分类结果；\n37\n华南理工大学硕士学位论文\nc) 把错误分类的窗口保存起来，即为困难样本。","keyWordsShow":"\u003cem\u003e过访问路径下的正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为-1）。\u003c/em\u003e","title":"驾驶员辅助系统中基于视觉的行人检测算法研究","author":"蔡龙楷","comeFrom":"博硕","percentage":77.0084,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"01","organ":"华南理工大学","textid":"59226b0daaf029aaa744238da5f406cf","category":"CMFD","context":"于窗口大小为 32×64 像素的二类分类器\n1. 采用初始样本集，进行第一阶段的训练：\n1)分别计算\u003cem\u003e正样本和负样本的 HOG 特征，并对样本进行标记，例如正\n样本标记为 1，负样本标记为\u003c/em\u003e-1；\n2)创建 SVM 对象，并设置 SVM 类型、核函数的类型以及 SVM 参数的\n类型","copyCount":1.0,"copyKeywords":"驾驶员辅助系统;行人检测;机器学习;Haar;HOG","startIndex":19092.0,"endIndex":19138.0,"checkid":1.33210894E8,"isQueto":0.0,"md5Code":"a82d9d6ee4670f91dc2c7a5f10878b6f"}],"checkContent":"过访问路径下的正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为-1）。获取图片","endIndex":19142.0,"checkContentShow":"优化功能。\n4.2.1 获取图片信息功能的实现\n该功能主要就是通\u003cem\u003e过访问路径下的正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为-1）。获取图片\u003c/em\u003e信息功能代码的关键代码如下：\ndef get_image_lis","chapterNum":0.0,"index":40},"25303":{"num":45.0,"wordNum":105.0,"startIndex":25303.0,"color":"red","copyRate":72.1882,"yyIndexs":[],"list":[{"id":3.2382846E7,"fileName":"2019052108283143859751.doc","checkContent":"中所有帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷\u003cbr/\u003e中所有帮助过我，关心过我的领导、老师、同学和朋友\n致以最真诚的谢意","returnContent":"3 3\n \n致 谢 \n \n时光荏苒，还来不及感慨就已成回忆。 \n在人生最灿烂的季节里，我来到了重庆师范大学。在校的七年时间里，我学\n到了很多专业知识和做人的道理。在我成长的道路上，有无数的老师和同学给予\n我关怀和帮助。在毕业之际，我要真诚地向他们表达我深深地谢意。 \n本次毕业论文历时 8 个月，我的导师靳明全教授在这 8 个月中给了我无私的\n帮助和指导。他每天都忙碌不堪，但是我每次遇到问题向他请教时，他都从来不\n推辞，还保持一贯温和的态度，细心地为我讲解，并借给我许多珍贵的资料和书\n籍供我参考、查阅，给了我很多建设性的意见和建议。我的毕业论文，每一页都\n浸润着他的心血和汗水。从他身上，我不仅学到了专业知识，更看到了一位园丁\n高尚的品格和严谨的治学的态度。靳明全教授对我的言传身教我将终身铭记于\n心：对事一丝不苟，对人真诚热情。 \n当然，本专业的其它各位老师也给我的论文提出了许多宝贵的意见，同样感\n谢他们的帮助。还有我可爱的同学们，在一起走过的岁月里，我们互勉互励、共\n同进步，他们的友谊值得我永远珍藏。 \n路漫漫其修远兮！我知道，走出象牙塔后的我们还有很长的路要走。在我临\n行之前，我要向大学生活中所有帮助过我，关心过我的领导、老师、同学和朋友\n致以最真诚的谢意。我要向大学生活中所有帮助过我，关心过我的领导、老师、同学和朋友\n致以最真诚的谢意。我的人生篇章因为有你们而变得更加精彩！ \n最后，衷心地感谢在百忙之中评阅论文和参加答辩的各位专家、教授！ \n谨以此文文纪念我在母校重庆师范大学的七年大学生活。\n \n \n \n \n \n \n重庆师范大学硕士学位论文 独创性声明 \n3 4","keyWordsShow":"\u003cem\u003e个课题进行过程中所有帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷\u003c/em\u003e","title":"论郭沫若抗战史剧中的日本文化因素","author":"曹丹丹","comeFrom":"博硕","percentage":67.9386,"type":"red","periodical":"中国优秀博硕士学位论文全文数据库 (硕士)","periodicalYear":"2006","num":"10","organ":"重庆师范大学","textid":"0e8f882221fe76f077916f717d83e416","category":"CMFD","context":"珍藏。 \n路漫漫其修远兮！我知道，走出象牙塔后的我们还有很长的路要走。在我临\n行之前，我要向大学生活\u003cem\u003e中所有帮助过我，关心过我的领导、老师、同学和朋友\n致以最真诚的谢意\u003c/em\u003e。我要向大学生活中所有帮助过我，关心过我的领导、老师、同学和朋友\n致以最真诚的谢意。我的人生","copyCount":2.0,"copyKeywords":"郭沫若;日本文化;影响;抗战史剧","startIndex":25303.0,"endIndex":25342.0,"checkid":1.3321103E8,"isQueto":0.0,"md5Code":"8ac195cbea58dc81c73417d709a404e0"},{"id":3.2382844E7,"fileName":"2019052108283143859751.doc","checkContent":"所有帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷地感谢\u003cbr/\u003e所有帮助过我关心过的老师、同学表示由衷的谢意！\n最后，感谢","returnContent":"Junqin Gao Jujuan Gao, Xuewen Zhang，et al. 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labeling.2014.\n(Submitted).\n59\n干湿交替对若尔盖湿地枯落物和土壤有机质分解的影响\n60\n致谢\n致谢\n在即将毕业的日子里，我的心情难以言喻。在这里，我想对在这三年当中每一个\n帮助过关心过我的人，表达我诚挚的谢意。\n本论文的顺利完成，首先要感谢我的导师高俊琴老师的悉心指导。在论文的撰写\n过程中，导师高俊琴倾注了大量的精力。高老师一丝不苟的作风、严谨治学的态度使\n我受益匪浅。三年来，高老师不仅在学习上对我严格要求，更在生活上给予了真诚的\n关心，在此谨向导师表达我最崇高的谢意与最衷心的感激！祝愿高老师身体健康，工\n作顺利！\n此外，在论文开题时，北京林业大学自然保护区学院雷光春老师、张明祥老师、\n李红丽老师、张振明老师、雷霆老师提出了许多宝贵的意见和建议。在论文开题时，北京林业大学自然保护区学院雷光春老师、张明祥老师、\n李红丽老师、张振明老师、雷霆老师提出了许多宝贵的意见和建议。在此，向上述老\n师表达诚挚的谢意！\n在论文的成形过程中，感谢北京林业大学自然保护区学院硕保护区11班的同学\n们提供的帮助和支持！\n谨此，再再次向所有帮助过我关心过的老师、同学表示由衷的谢意！\n最后，感谢我的父母以及家人，我的每一份成绩都饱含你们的付出和支持！\n61","keyWordsShow":"\u003cem\u003e课题进行过程中所有帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷地感谢\u003c/em\u003e","title":"干湿交替对若尔盖湿地枯落物和土壤有机质分解的影响","author":"张雪雯","comeFrom":"博硕","percentage":56.7702,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"12","organ":"北京林业大学","textid":"323063d1b8c9d163bb527c15ab60ec69","category":"CMFD","context":"成形过程中，感谢北京林业大学自然保护区学院硕保护区11班的同学\n们提供的帮助和支持！\n谨此，再再次向\u003cem\u003e所有帮助过我关心过的老师、同学表示由衷的谢意！\n最后，感谢\u003c/em\u003e我的父母以及家人，我的每一份成绩都饱含你们的付出和支持！\n61","copyCount":2.0,"copyKeywords":"若尔盖湿地;泥炭土;干湿交替;碳排放;激发效应","startIndex":25304.0,"endIndex":25345.0,"checkid":1.3321103E8,"isQueto":0.0,"md5Code":"8ac195cbea58dc81c73417d709a404e0"},{"id":3.2382845E7,"fileName":"2019052108283143859751.doc","checkContent":"帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷地感谢各位答辩老师\u003cbr/\u003e帮助过我的老师、同学、亲人及朋友表示我最最真诚的谢意!\n最后，感谢各位参加论文答辩和评审的老师","returnContent":"在许多人的支持和帮助下，我顺利的完成了我的课题，\n在此表示感谢。\n 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Technologies (EIDWT-2013), Xi\u0027an, September 9-11, 2013. pp.350-355.\n43\n致 谢\n光阴似箭，岁月如梭，三年的研究生学习生活也即将随着学位论文的完成画上句号。\n回顾在暨南大学的求学时光，需要感谢的人太多，限于篇幅，不能一一列出，还望见谅！\n首先，我要感谢导师谭晓青老师。谭老师从论文的方向选定，到论文的完成，都对\n我进行了耐心的指导。我刚开始学用 latex 写论文的时候经常遇到困难，谭老师细心地检\n查并帮我修改。这给了我很大的信心。谭老师一丝不苟的工作态度，诲人不倦的师者风范，\n严谨求实的治学风格，给我留下了深刻的印象。这一直鼓励着我，激励着我不断进取。谨\n向导师谭晓青老师致以崇高的敬意和衷心的感谢。\n感谢暨南大学以博大包容的情怀胸襟培育着我，暨大将是我心中永远的骄傲。感谢\n信息技术学院数学系所有的老师和同学对我的帮助和指导。\n感谢各位评委老师在百忙之中抽出宝贵的时间对我的论文进行评审并出席答辩！\n感谢11级数学系的全体同学，是他们陪伴我走过三年愉快而充实的研究生生活！研究\n生三年中我最快乐的回忆中总有他们的身影。辩！\n感谢11级数学系的全体同学，是他们陪伴我走过三年愉快而充实的研究生生活！研究\n生三年中我最快乐的回忆中总有他们的身影。\n感谢这篇论文所涉及到的各位学者。本文引用了数位学者的研究文献，如果没有各位\n学者的研究成果的帮助和启发，我将很难完成本篇论文的写作。。\n由于我的学术水平有限，所写论文难免有不足之处，恳请各位老师和学友批评和指正！\n姜莲霞\n2014 年 5 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月于暨南\n44","copyCount":1.0,"copyKeywords":"量子密秘共享;Bell态;量子隐形传态;可控量子隐形传态;广义GHZ态","startIndex":25353.0,"endIndex":25409.0,"checkid":1.33211031E8,"isQueto":0.0,"md5Code":"26bb5294af20ac4a758256b708c9774f"},{"id":3.2382815E7,"fileName":"2019052108283143859751.doc","checkContent":"由于我的学术水平有限，论文中难免有疏漏和不足之处，恳请各位老师给予批评和指正\u003cbr/\u003e由于我的学术水平有限，所写论文难免有不足之处，恳请各位老师和学友批评和\n指正","returnContent":"尤其要感谢我的论文指导老师——迟德强，\n他对我进行了无私的指导和帮助，不厌其烦的帮助进行论文的修改和改进。另外，在\n校图书馆查找资料的时候，图书馆的老师也给我提供了很多方便与帮助。在此向指导\n和帮助过我的各位老师表示最衷心的感谢！\n同时感谢这篇论文所涉及到的各位学者。本文引用了数位学者的研究文献，如果\n没有各位学者之研究成果的帮助和启发，我将很难完成本篇论文的写作。\n再要感谢我的同学和朋友，在我写论文的过程中给予我很多有用的素材，还有在\n论文撰写和排版过程中提供的热情帮助。\n由于我的学术水平有限，所写论文难免有不足之处，恳请各位老师和学友批评和\n指正！\n柴文娟\n2012年3月12日\n44\n学位论文评阅及答辩情况表\n姓名是否硕导所在单位\n职 务义\n 论 \n文\n评 \n阅\n人 \n专业技术\n姓名 务A 是否硕导 所在单位\n职 \n主席\n答\n辩 \n委\n员委 \n会\n成 \n员\n员\niSmSi答辩秘书、I答辩日期I\n 备注 \n※优秀为\"A\"；良好为\"B\"\";合格为\"C\"；不合格为D\\","keyWordsShow":"\u003cem\u003e由于我的学术水平有限，论文中难免有疏漏和不足之处，恳请各位老师给予批评和指正\u003c/em\u003e","title":"中国监狱服刑人员之人权保障探析","author":"柴文娟","comeFrom":"博硕","percentage":72.1882,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2012","num":"02","organ":"山东大学","textid":"d6677d9b07a121fe66570571be3fc7d7","category":"CMFD","context":"的同学和朋友，在我写论文的过程中给予我很多有用的素材，还有在\n论文撰写和排版过程中提供的热情帮助。\n\u003cem\u003e由于我的学术水平有限，所写论文难免有不足之处，恳请各位老师和学友批评和\n指正\u003c/em\u003e！\n柴文娟\n2012年3月12日\n44\n学位论文评阅及答辩情况表\n姓名是否硕导所在单位\n职 ","copyCount":1.0,"copyKeywords":"监狱;服刑人员;人权保障;应有权利;法定权利;实有权利","startIndex":25371.0,"endIndex":25409.0,"checkid":1.33211031E8,"isQueto":0.0,"md5Code":"26bb5294af20ac4a758256b708c9774f"}],"checkContent":"个课题进行过程中所有帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷地感谢各位答辩老师能够抽出宝贵时间对我论文的内容进行评阅。由于我的学术水平有限，论文中难免有疏漏和不足之处，恳请各位老师给予批评和指正","endIndex":25409.0,"checkContentShow":"有他们的研究成果，我完成本次论文的难度将大大增加。\n最后，对在整\u003cem\u003e个课题进行过程中所有帮助过我的领导、老师、同学等表示我最真诚的谢意！\n同时由衷地感谢各位答辩老师能够抽出宝贵时间对我论文的内容进行评阅。由于我的学术水平有限，论文中难免有疏漏和不足之处，恳请各位老师给予批评和指正\u003c/em\u003e！\n参考文献\n[1] 佚名. Python学习手册[M]. 20","chapterNum":0.0,"index":45},"5415":{"num":8.0,"wordNum":39.0,"startIndex":5415.0,"color":"red","copyRate":55.1729,"yyIndexs":[],"list":[{"id":3.2383814E7,"fileName":"2019052108283143859751.doc","checkContent":"编程语言中的老大哥了。\n2.1.1 Python语言的优点\nPython语\u003cbr/\u003e有前途的技术背后的主流语言,Python近年来...C/C++作为编程语言的老大哥","returnContent":"2019年4月10日-C实际上是编程语言的通用语言,由它催生出了...一些最有前途的技术背后的主流语言,Python近年来...C/C++作为编程语言的老大哥,需要严谨的逻辑思维...","keyWordsShow":"\u003cem\u003e以算的上是编程语言中的老大哥了。\n2.1.1 Python语言的优点\nPython语\u003c/em\u003e","title":"2019哪一种编程语言发展“钱”景更好?10大主流编程语言分析","comeFrom":"互联网","percentage":55.1729,"type":"red","category":"NET","context":"2019年4月10日-C实际上是编程语言的通用语言,由它催生出了...一些最\u003cem\u003e有前途的技术背后的主流语言,Python近年来...C/C++作为编程语言的老大哥\u003c/em\u003e,需要严谨的逻辑思维...","copyCount":6.0,"startIndex":5415.0,"endIndex":5457.0,"checkid":1.3322067E8,"isQueto":0.0,"md5Code":"95bce3ff1e47a59e1810a77d8ddb1ead"}],"checkContent":"以算的上是编程语言中的老大哥了。\n2.1.1 Python语言的优点\nPython语","endIndex":5457.0,"checkContentShow":"由Python之父Guido van Rossum正式发布的，可\u003cem\u003e以算的上是编程语言中的老大哥了。\n2.1.1 Python语言的优点\nPython语\u003c/em\u003e言有以下优点：\n（1）语法简单易学\n使代码具有高度的可读性是Py","chapterNum":0.0,"index":8},"25244":{"num":44.0,"wordNum":37.0,"startIndex":25244.0,"color":"red","copyRate":55.8722,"yyIndexs":[],"list":[{"id":3.2382777E7,"fileName":"2019052108283143859751.doc","checkContent":"引用了多位学者的论文内容，对我起到了很大的帮助，没有他们的研究成果，我\u003cbr/\u003e引用了多位学者的研究文献献，\n你们的研究成果对我论文的写作起到了较大的帮助","returnContent":"Transitioning Societies in the Developing World:The Philippines. Advances in Developing Human Resources,8(1): 46-61.\n[43]E. Cho, G. N. McLean (2004), What we discovered about NHRD and what it means for HRD. Advances in Developing Human Resources,6(3):383-393.\n[44]S. Lavenex (2007), The competition state and highly skilled migration. Society vol 44(2):32-41.致谢高层次创新人才引进研究一以启东为例\n 致谢\n历时两个多月的时间终于将这篇论文写完，在论文的写作过程中遇到了不少\n的困难和障碍，都在同学、同事、家人和老师的帮助下顺利克服。\n感谢我的导师章小波副教授，章老师严谨的治学精神和精益求精的工作作风\n给我留下了深刻的印象，在论文写作过程中对我进行了无私的指导和帮助；初稿\n完成后，她又在百忙之中抽出时间对论文进行认真审阅，不厌其烦地提出修改意\n见，让我的论文逐步成型。她又在百忙之中抽出时间对论文进行认真审阅，不厌其烦地提出修改意\n见，让我的论文逐步成型。整个写作过程让我的思维得到了充分的锻炼，视野得\n到了很大的拓宽，使我受益匪浅。\n感谢我的同学和同事，在我论文写作过程中提供了很多素材，为论文的成功\n完成打下扎实的基袖。\n感谢这篇论文所涉及到的各位学者，本文参考和引用了多位学者的研究文献献，\n你们的研究成果对我论文的写作起到了较大的帮助和启发。\n最后感谢所有支持和帮助过我的人，愿你们一切如意，幸福快乐！\n44","keyWordsShow":"\u003cem\u003e文中引用了多位学者的论文内容，对我起到了很大的帮助，没有他们的研究成果，我\u003c/em\u003e","title":"高层次创新人才引进研究","author":"单剑峰","comeFrom":"博硕","percentage":55.8722,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2012","num":"05","organ":"苏州大学","textid":"e02f934442fff1bf45e173241ce17271","category":"CMFD","context":"过程中提供了很多素材，为论文的成功\n完成打下扎实的基袖。\n感谢这篇论文所涉及到的各位学者，本文参考和\u003cem\u003e引用了多位学者的研究文献献，\n你们的研究成果对我论文的写作起到了较大的帮助\u003c/em\u003e和启发。\n最后感谢所有支持和帮助过我的人，愿你们一切如意，幸福快乐！\n44","copyCount":1.0,"copyKeywords":"高层次创新人才;人才引进;对策","startIndex":25244.0,"endIndex":25281.0,"checkid":1.33211029E8,"isQueto":0.0,"md5Code":"f5c2cfac8ae501ff4427a82b610c927e"}],"checkContent":"文中引用了多位学者的论文内容，对我起到了很大的帮助，没有他们的研究成果，我","endIndex":25281.0,"checkContentShow":"校图书馆和数据库提供了良好的学习条件和丰富的学习资料。\n此外，本\u003cem\u003e文中引用了多位学者的论文内容，对我起到了很大的帮助，没有他们的研究成果，我\u003c/em\u003e完成本次论文的难度将大大增加。\n最后，对在整个课题进行过程中所有","chapterNum":0.0,"index":44},"15042":{"num":37.0,"wordNum":74.0,"startIndex":15042.0,"color":"red","copyRate":60.8211,"yyIndexs":[],"list":[{"id":3.238268E7,"fileName":"2019052108283143859751.doc","checkContent":"模块，CV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类\u003cbr/\u003e模块、CvAux模块。CV模块包含常用到的\n图像处理函数和一些高级的机器视觉算法；ML是机器学习库，包含一些基于统计的分\n类","returnContent":"可以更加灵活的使用在图像处理过程中，它虽然可以使用外部\n库，但是觉不依赖外部库。OpenCV包含了五个基本的图像处理的模块，分别为CV模\n块、MLL模块、HighGUI模块、CXCORE模块、CvAux模块。CV模块包含常用到的\n图像处理函数和一些高级的机器视觉算法；ML是机器学习库，包含一些基于统计的分\n类和聚类工具；HighGUI包含图像和视频输入/输出的函数；CXCore包含Open CV的所\n用到的基本的数据结构和相关函数，如图5.7所示。在图中并没有包含CvAux模块，在\n这个模块中包含了一些已经不再使用的算法（例如基于嵌入式隐马尔科夫模型的人脸识\n别算法）以及一些还处于实验阶段的算法。本文所实现的插件机视觉定位系统软件中的\n图像处理部分主要利用了 OpenCV的函数库，完成了图像的预处理过程以及工位点的\n识别过程。\n-37 -\n基于机器视觉的视插件机定位系统的研究与应用 \na High Gin\n�像处理和视 阁像视频输入/\n觉躲 输出\nI CXCORE\nI构和孩法、XML支持、绘阁满数\n ，本结图5. 7 Open CV基本结构\nFig.5.7 Basic structure of Open CV\n(2) XML: XML是一种界定文本数据的简便而标准的方法。","keyWordsShow":"\u003cem\u003e中有两大重要模块，CV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。\u003c/em\u003e","title":"基于机器视觉的插件机定位系统的研究与应用","author":"宋佳星","comeFrom":"博硕","percentage":58.2596,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"07","organ":"大连理工大学","textid":"969ecfd708d4b221e310555727817f4e","category":"CMFD","context":"nCV包含了五个基本的图像处理的模块，分别为CV模\n块、MLL模块、HighGUI模块、CXCORE\u003cem\u003e模块、CvAux模块。CV模块包含常用到的\n图像处理函数和一些高级的机器视觉算法；ML是机器学习库，包含一些基于统计的分\n类\u003c/em\u003e和聚类工具；HighGUI包含图像和视频输入/输出的函数；CXCore包含Open 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分类方法，训练得出笑脸分类器。","keyWordsShow":"\u003cem\u003e中有两大重要模块，CV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等\u003c/em\u003e","title":"视频序列中的笑脸识别技术研究","author":"刘娇","comeFrom":"博硕","percentage":48.0172,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"S2","organ":"西安电子科技大学","textid":"a1ebe336a7b3df2b2e4f02dbb049663d","category":"CMFD","context":"的是实时应用。它采\n用优化的 C 代码编写，这样能够充分利用多核处理器的优势。\nOpenCV 的主体\u003cem\u003e主要分为五个模块，其中，CV 模块主要包含的是基本图像处\n理的函数，和高级计算机视觉的相关算法。ML 模块是机器学习库，其中包含基于\n统计方面的分类、聚类等\u003c/em\u003e工具。HighGUI 模块主要包含对图像和视频进行输入/输\n出的相关函数。CXCore模块包","copyCount":1.0,"copyKeywords":"光流;金字塔梯度方向直方图;笑脸识别;视频环境","startIndex":15042.0,"endIndex":15115.0,"checkid":1.33210826E8,"isQueto":0.0,"md5Code":"fb2149734bf2c2f3d2e47adc8743ae60"},{"id":3.2383834E7,"fileName":"2019052108283143859751.doc","checkContent":"模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类\u003cbr/\u003eCV模块包含基本的图像处理函数和高级的计算机视觉算法。ML是机器学习库,包含一些基于统计的分类","returnContent":"2017年4月12日-OpenCV的CV模块包含基本的图像处理函数和高级的计算机视觉算法。ML是机器学习库,包含一些基于统计的分类和聚类工具。HighGUI包含图像和视频输...","keyWordsShow":"\u003cem\u003eV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。\u003c/em\u003e","title":"[作业]OPENCV人脸识别_图文-共享资料网","comeFrom":"互联网","percentage":60.8211,"type":"red","category":"NET","context":"2017年4月12日-OpenCV的\u003cem\u003eCV模块包含基本的图像处理函数和高级的计算机视觉算法。ML是机器学习库,包含一些基于统计的分类\u003c/em\u003e和聚类工具。HighGUI包含图像和视频输...","copyCount":1.0,"startIndex":15052.0,"endIndex":15116.0,"checkid":1.33220885E8,"isQueto":0.0,"md5Code":"cd06d7cde19b1c5f9843956b3c7658b1"},{"id":3.2382682E7,"fileName":"2019052108283143859751.doc","checkContent":"包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类\u003cbr/\u003e包含了各种基本的图像处理函数和高级的计算机\n视觉算法。\n（2）ML 模块：机器学习库，包含了一些基本统计的分类","returnContent":"公司开发的一个计算机视觉工具箱，采用 C/C++语\n言编写，可以运行在 Linux、Windows 和 Mac 等操作系统。此外，OpenCV 还提供了\nPython、Ruby、MATLAB 以及其他语言的接口，使用起来非常的方便。目前最新的\n官方版本号是 OpenCV2.4.2。\n2.2.2 OpenCV 的结构模块及内容\nOpenCV 主体分为五大模块，其中四大模块如图 2.2 所示。\n8\n第 2 章 视觉跟踪测控总体研究方案\nCVMLLHighGUI\n图像处理与视觉算法统计分类器GUI，\n图像和视频输入/输出\nCXCORE\n基本结构和算法，XML支持，绘图函数\n图 2.2 OpenCV 的基本结构\n（1）CV 模块：核心函数库，包含了各种基本的图像处理函数和高级的计算机\n视觉算法。\n（2）ML 模块：机器学习库，包含了一些基本统计的分类与聚类工具。\n（3）HighGUI 模块：GUI 函数库，包含了图像以及视频的输入、输出函数。\n（4）CXCORE 模块：数据结构与线性代数库，包含了 OpenCV 的一些基本数据\n结构以及功能定义，同时包含数据处理的相关函数。\n2.","keyWordsShow":"\u003cem\u003eV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。\u003c/em\u003e","title":"移动机器人自主视觉跟踪测控技术研究","author":"龙忠杰","comeFrom":"博硕","percentage":50.6875,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"07","organ":"北京信息科技大学","textid":"2a6acc57319829235964b252b71a5edc","category":"CMFD","context":"构和算法，XML支持，绘图函数\n图 2.2 OpenCV 的基本结构\n（1）CV 模块：核心函数库，\u003cem\u003e包含了各种基本的图像处理函数和高级的计算机\n视觉算法。\n（2）ML 模块：机器学习库，包含了一些基本统计的分类\u003c/em\u003e与聚类工具。\n（3）HighGUI 模块：GUI 函数库，包含了图像以及视频的输入、输出函数","copyCount":1.0,"copyKeywords":"移动机器人;目标跟踪;特征点提取;立体匹配;OpenCV","startIndex":15062.0,"endIndex":15116.0,"checkid":1.33210826E8,"isQueto":0.0,"md5Code":"fb2149734bf2c2f3d2e47adc8743ae60"},{"id":3.2382677E7,"fileName":"2019052108283143859751.doc","checkContent":"算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类\u003cbr/\u003e算法。ML模块是一个机器学习库，其中含有一盛基于统计的聚类和分\n类","returnContent":"Python、Ruby、Matlab等多种编程访p的接\n丨：】，实现了计算机视觉和�像处理巾的很多通川的兑法，具高效的性质。\nCV MLL HighGUI\n像和计算机视觉算法 统计分类器 阁像和视频输入/输出\nV\\7V\n CXCORE\n猫本结构和兑法，XML支持，绘制m数\n图2.S OpcnCV基本结构\nOpcnCV itfr五个模块，111十CvAux模块足一个动态的模块，{iif^?了一^?快被\n14\n岛大学硕:学位论文 :1 \n淘汰的函数和算法，还包含--些还在实验阶段的函数和算法。所以在图2.5中并没\n有将CvAux模块展示出来，只仅含CV、MULL. HighGUI和CXCORE四个模块。\n其中CV模块是图像和计算机视觉算法模块，包含了常用的图像处理函数和高级的\n计算机视觉算法。ML模块是一个机器学习库，其中含有一盛基于统计的聚类和分\n类工具。视频和图像的输入/输出函数在HighGUI模块中。CXCore模块则包含\nOpenCV中的一些基本结构和算法以及一些绘制的函数。\n2.4小结\n本章主要介绍了实现基于手势识别的人机交互系统中所涉及到的主要技术，首\n先介绍了手势识别的基本的内容，然后对颜色空间进行了介绍，说明了使用YCgCr\n颜色空间进行手势识别的优点，紧接着介绍了手势分割和手势分类识别以及运动检\n测中所用到的关键技术，最后介绍了最为常用的计算机视觉包OpenCV。","keyWordsShow":"\u003cem\u003e于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。\u003c/em\u003e","title":"基于单目视觉的手势识别的研究与应用","author":"王艺婷","comeFrom":"博硕","percentage":56.7526,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"S1","organ":"青岛大学","textid":"417f69427bce3d550b16a8d5ffba03db","category":"CMFD","context":"RE四个模块。\n其中CV模块是图像和计算机视觉算法模块，包含了常用的图像处理函数和高级的\n计算机视觉\u003cem\u003e算法。ML模块是一个机器学习库，其中含有一盛基于统计的聚类和分\n类\u003c/em\u003e工具。视频和图像的输入/输出函数在HighGUI模块中。CXCore模块则包含\nOpenCV","copyCount":1.0,"copyKeywords":"手势识别;肤色模型;背景模型;凸缺陷","startIndex":15073.0,"endIndex":15116.0,"checkid":1.33210826E8,"isQueto":0.0,"md5Code":"fb2149734bf2c2f3d2e47adc8743ae60"}],"checkContent":"中有两大重要模块，CV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。","endIndex":15116.0,"checkContentShow":"，在Windows、Linux、Mac等操作系统上都可以运行。其\u003cem\u003e中有两大重要模块，CV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。\u003c/em\u003e同时OpenCV具有图像和强大的矩阵运算能力，提供了非常灵活的用","chapterNum":0.0,"index":37},"14833":{"num":35.0,"wordNum":87.0,"startIndex":14833.0,"color":"red","copyRate":77.9906,"yyIndexs":[],"list":[{"id":3.2382686E7,"fileName":"2019052108283143859751.doc","checkContent":"矩形特征、四矩形特征。这种特征是使用黑白两色的矩形来进行描述的，特征值就是特征中白色矩形像素和减去黑色矩形的像素和\u003cbr/\u003e矩形特征，其中，A、B为双矩形特征，\n主要用来描述边缘特征，其特征值定义为白色矩形像素和减去黑色矩形像素和","returnContent":"联在一起，让被检测人脸图像依次通过这些强分类器，形成级联分类器。该算\n法利用“积分图”的方法快速的计算出人脸图像的简单特征，再利用Adaboost\n学习算法的学习能力，从一些特征简单的弱分类器中学习，并通过这些弱分类\n器的线性组合，构造成出一种较强的分类器，最后通过Cascade级联算法将单个\n分类器级联在一起，让被检测人脸图像一级一级地通过各个分类器，从而，形\n成一个复杂精确的人脸检测器，本文最终使用的级联分类器是由英特尔?公司\n提供的开源代码。 \n4.1 图像矩形特征与积分图值 \n4.1.1 人脸图像矩形特征 \n人脸图像的矩形特征[27]是一种能有效地区别人脸与非人脸的特征，通常把\n它称为类Haar特征。图4-1中给出了三种矩形特征，其中，A、B为双矩形特征，\n主要用来描述边缘特征，其特征值定义为白色矩形像素和减去黑色矩形像素和。\nC为三矩形特征，用来描述线性特征，其值等于两边的两个白色矩形的像素和\n减去中间的黑色矩形的像素和。D为四矩形特征，用来描述中间与周边的特性，\n其值为对角线上矩形内像素和之差。 \n \n图 4-1 矩形特征图 \n利用Adaboost算法对前两个矩形特征进行训练，图4-2中所示，第一行为\n矩形特征滤波器，分别用这两个滤波器来检测人脸图像，第二行第一列为待检\n测人脸图像，由于人眼部区域亮度低于脸颊，双眼亮度低干眉心的特点，通过\n横向和纵向的比较能大体确定人脸的基本位置。","keyWordsShow":"\u003cem\u003e矩形特征、四矩形特征。这种特征是使用黑白两色的矩形来进行描述的，特征值就是特征中白色矩形像素和减去黑色矩形的像素和\u003c/em\u003e","title":"人脸识别技术及其在汽车防盗中的应用","author":"李世兵","comeFrom":"博硕","percentage":48.7181,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2010","num":"05","organ":"合肥工业大学","textid":"0bcb22418efa5ee3b5b26081b1a61f52","category":"CMFD","context":"特征[27]是一种能有效地区别人脸与非人脸的特征，通常把\n它称为类Haar特征。图4-1中给出了三种\u003cem\u003e矩形特征，其中，A、B为双矩形特征，\n主要用来描述边缘特征，其特征值定义为白色矩形像素和减去黑色矩形像素和\u003c/em\u003e。\nC为三矩形特征，用来描述线性特征，其值等于两边的两个白色矩形的像素和\n减去中间的黑色矩形","copyCount":1.0,"copyKeywords":"人脸识别;TMS320DM6446;达芬奇技术;Adaboost人脸检测;PCA的人脸识别;汽车防盗","startIndex":14833.0,"endIndex":14890.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"},{"id":3.2382683E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征的缺点就是只能对一些\u003cbr/\u003e特征\n值为白色矩形像素和减去黑色矩形像素和。Haar特征值反映了图像的灰度变化情\n况。例如：脸部的一些特征能由矩形特征","returnContent":"的是输入矩形特征的方式，矩形特征分为三类：两矩形特征，三矩形特征和四矩形\n特征。两矩形特征反映的是边缘特征，三矩形特征反映的是线性特征、四矩形特征\n反映的是特定方向特征。特征模板内有白色和黑色两种矩形，并定义该模板的特征\n值为白色矩形像素和减去黑色矩形像素和。Haar特征值反映了图像的灰度变化情\n况。例如：脸部的一些特征能由矩形特征简单的描述，如：眼睛要比脸颊颜色要深，\n鼻梁两侧比鼻梁颜色要深，嘴巴比周围颜色要深等。但矩形特征只对一些简单的图\n形结构，如边缘、线段较敏感，所以只能描述特定走向（水平、垂直、对角）的结\n构。\nLBP (Local Binary Pattern，局部二值模式）是由 T. Ojala，M.Pietik^inen，\n和D. Harwood首先提出的一种用来描述图像局部纹理特征的算子，具有灰度不变\n性。LBP算子是在一个3*3的窗口内，中心像素为阈值，如果除中心以外的像素点\n低于阈值，则标记为0,否则为1.由此可以产生一个8位二进制的LBP数值，这个\n数值反映了中心像素点的纹理信息。因为最初提出的LBP算子覆盖的区域很小，所\n以Ojala等人对LBP算子进行了改进，把之前的3*3正方形区域扩大为�*个任意大\n小的圆形邻域，这样可以达到适应不同尺度的纹理尺度的要求。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征的缺点就是只能对一些\u003c/em\u003e","title":"基于卷积神经网络的水果检测研究","author":"侯蕾","comeFrom":"博硕","percentage":65.1873,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"08","organ":"福建师范大学","textid":"c540fb28d35c1f9a68c43c39dc2d0378","category":"CMFD","context":"征反映的是线性特征、四矩形特征\n反映的是特定方向特征。特征模板内有白色和黑色两种矩形，并定义该模板的\u003cem\u003e特征\n值为白色矩形像素和减去黑色矩形像素和。Haar特征值反映了图像的灰度变化情\n况。例如：脸部的一些特征能由矩形特征\u003c/em\u003e简单的描述，如：眼睛要比脸颊颜色要深，\n鼻梁两侧比鼻梁颜色要深，嘴巴比周围颜色要深等。但矩形","copyCount":1.0,"copyKeywords":"水果检测;卷积神经网络;选择搜索性算法;熵值分析;尺寸约束","startIndex":14865.0,"endIndex":14920.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"},{"id":3.2382825E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征\u003cbr/\u003e特征值为白色矩形像素和减去黑色矩形像素和。Haar特征值反映了图像的灰度变化情况","returnContent":"+(1-γ)1ni|∑yi\u003d1(f k(x i)-μ1)2(10)以上是Online MIL的实现原理,算法实现流程如图1所示。可以看出,在跟踪过程中,特征选择的好坏直接影响到跟踪的准确度和稳定性。在Babenko等人[7-8,11-12]的MIL算法和本文算法中,都使用Haar特征进行特征的选择,以下简单介绍一下Haar特征。Haar特征分为4类:边缘特征、线性特征、中心特征和对角线特征,组合成特征模板,如图2所示。特征模板内有白色和黑色两种矩形,并定义该模板的特征值为白色矩形像素和减去黑色矩形像素和。Haar特征值反映了图像的灰度变化情况。对于图2a、图2b和图2d这类特征,特征数值计算公式为:v\u003dSum白-Sum黑,而对于图2c来说,计算公式为:v\u003dSum白-2×Sum黑;通过改变特征模板的大小和位置,可在图像子窗口中穷举出大量特征。图2的特征模板称为“特征原型”;特征原型在图像子窗口中扩展(平移伸缩)得到的特征称为“矩形特征”;矩形特征的值称为“特征值”。Haar特征的计算主要是积分图方法,积分图就是只遍历一次图像就可以求出图像中所有区域像素和的快速算法,大大提高了图像特征值计算的效率,因此使用不同类型的haar特征进行特征值的求取,不会影响计算的速度。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征\u003c/em\u003e","title":"TLD目标跟踪算法研究","author":"高帆 吴国平 刑晨 马丽","comeFrom":"期刊","percentage":68.6513,"type":"red","periodical":"电视技术","periodicalYear":"2013","vol":"v.37;No.413","beginPage":76.0,"endPage":80.0,"num":"11","organ":"电视技术","textid":"587c10530c4031363af46af822504cdf","category":"CJFD","context":"征、中心特征和对角线特征,组合成特征模板,如图2所示。特征模板内有白色和黑色两种矩形,并定义该模板的\u003cem\u003e特征值为白色矩形像素和减去黑色矩形像素和。Haar特征值反映了图像的灰度变化情况\u003c/em\u003e。对于图2a、图2b和图2d这类特征,特征数值计算公式为:v\u003dSum白-Sum黑,而对于图2","copyCount":1.0,"copyKeywords":"TLD;Online MIL;目标跟踪","startIndex":14865.0,"endIndex":14910.0,"checkid":1.33210821E8,"isQueto":0.0,"md5Code":"92958818c469bdc785290547c2a3b0fb"},{"id":3.2382685E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征\u003cbr/\u003e特征值为白色矩形像素和减去黑色矩形像素和。Haar 特征值反映了图像的\n灰度变化情况。但矩形特征","returnContent":"e 特征最早是由\nPapageorgiou 等人[27]应用于人脸表示共有三种形式的特征：边缘特征、中心特征和对\n角线特征。Viola 和 Jones[29]在此基础上，加入了线性特征，使 Haar 特征变为 3 种类\n型 4 种特征的形式，并组合成特征模板。特征模板内有白色和黑色两种矩形，并定义\n该模板的特征值为白色矩形像素和减去黑色矩形像素和。Haar 特征值反映了图像的\n灰度变化情况。但矩形特征只对一些简单的图形结构，如边缘、线段较敏感，所以只\n能描述特定走向（水平、垂直、对角）的结构。\n在传统目标检测算法中，支持向量机（Support Vector Machine，SVM）和\nAdaboosting 最常用的分类框架。在得到前述的各种特征之后，把这些特征送入 SVM\n或是 Adaboosting 中进行简单的训练，即可得到良好的效果。Viola[29]提出基于中国民航大学硕士学位论文\n11\nAda Boost 算法框架，使用 Haar-like 小波特征分类，然后采用滑动窗口搜索策略实现\n准确有效地人脸检测。Dalal[24]提出使用 HOG 作为特征，利用 SVM）作为分类器进\n行行人检测，也得到了很好的效果。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征\u003c/em\u003e","title":"深度学习在输电铁塔关键部件缺陷检测中的应用研究","author":"王子昊","comeFrom":"博硕","percentage":77.9906,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2018","num":"10","organ":"中国民航大学","textid":"c1bc28cd3f4b753aba197d6a4abc2249","category":"CMFD","context":" 3 种类\n型 4 种特征的形式，并组合成特征模板。特征模板内有白色和黑色两种矩形，并定义\n该模板的\u003cem\u003e特征值为白色矩形像素和减去黑色矩形像素和。Haar 特征值反映了图像的\n灰度变化情况。但矩形特征\u003c/em\u003e只对一些简单的图形结构，如边缘、线段较敏感，所以只\n能描述特定走向（水平、垂直、对角）的结构","copyCount":1.0,"copyKeywords":"图像处理;缺陷检测;深度学习;卷积神经网络;绝缘子;销钉","startIndex":14865.0,"endIndex":14910.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"},{"id":3.2382826E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003cbr/\u003e特征值定义为：白色矩形像素和减去黑色矩形像素和。\n按照这个定义，可知 Haar 特征值反映了图像的灰度变化情况","returnContent":"特征特征有三种：两矩形特征、三矩形特征、四矩形特征，在上\n图中 A 和 B 为两矩形特征，C 为三矩形特征，D 为四矩形特征。同样大小的两个\n矩形特征有水平和垂直两种方式，如图 A 和 B 所示。特征模板内有白色和黑色两\n种矩形像素，特征模板的特征值定义为：白色矩形像素和减去黑色矩形像素和。\n按照这个定义，可知 Haar 特征值反映了图像的灰度变化情况，但基本 Haar 特征只\n对一些边缘、线段起作用，只能描述特定走向（水平、垂直、对角）的结构。\n基本haar特征对于旋转很敏感，为了达到旋转不变性，Rainer Lienhart和Jochen\nMaydt 提出了扩展的 Haar 特征，引入了具有 45 度旋转的 Haar 特征和中心矩形特\n征。扩展后的特征大致分为 4 种类型：边缘特征、线特特征、中心环绕特征和特\n定方向特征：\n14 第二章 Viola-Jones 目标检测\n1.边缘特征 2.线性特征\n3.中心环绕特征 4.特定方向特征\n图 2.4 扩展的 Haar 矩形特征模板。\n通过改变特征模板的大小和位置，可在图像子窗口中得到大量的特征。上图\n的特征模板称为“特征原型”；特征原型在图像子窗口中扩展（平移伸缩）得到的\n特征称为“矩形特征”；矩形特征的值称为“特征值”。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003c/em\u003e","title":"基于Android平台的视觉手势识别研究","author":"王赞超","comeFrom":"博硕","percentage":62.4953,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"S2","organ":"西安电子科技大学","textid":"bc048e7d767c5c85ce0757e94a146222","category":"CMFD","context":"形特征有水平和垂直两种方式，如图 A 和 B 所示。特征模板内有白色和黑色两\n种矩形像素，特征模板的\u003cem\u003e特征值定义为：白色矩形像素和减去黑色矩形像素和。\n按照这个定义，可知 Haar 特征值反映了图像的灰度变化情况\u003c/em\u003e，但基本 Haar 特征只\n对一些边缘、线段起作用，只能描述特定走向（水平、垂直、对角）的结","copyCount":1.0,"copyKeywords":"Android;手势检测;肤色分割;手势识别;K均值聚类","startIndex":14865.0,"endIndex":14908.0,"checkid":1.33210821E8,"isQueto":0.0,"md5Code":"92958818c469bdc785290547c2a3b0fb"},{"id":3.2382689E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003cbr/\u003e特征值为白色矩形像素和\n减去黑色矩形像素和。\nHaar-like 特征值反映了图像的灰度变化情况。将上面的任意一个矩形","returnContent":"后来，Viola 和 Jones 在此基础上，使用多种类型多种\n形式特征进行人脸检测。\nHaar-like 特征即 Haar 特征，是计算机领域的一种常用特征描述子[28]。目前常用的\nHaar-like 特征可以分为四类：边缘特征、点特征（中心特征）、对角线特征和线性特征。\n这是由 Lienhart R.等对特征库扩展得到的。这些特征以不同类型不同形式组合成不同的\n特征模板。特征模板内有白色和黑色两种矩形，定义该模板的特征值为白色矩形像素和\n减去黑色矩形像素和。\nHaar-like 特征值反映了图像的灰度变化情况。将上面的任意一个矩形放到人脸区域\n上，然后，将白色区域的像素和减去黑色区域的像素和，得到的值称之为人脸特征值。\n因为矩形特征的位置和大小可以改变，故而一个检测子窗口包含非常多的矩形特征。为\n了快速计算这些特征值使用积分图方法，当要计算某个区域的像素很时可以直接索引像\n8\n素元素，做简单的加减法即可求出图像特征值。\n2.2 HOG 特征\nHOG 是 Histogram of Oriented Gradient 的英文缩写，中文翻译是方向梯度直方图，\n是计算机视觉中用来物体检测的特征描述子[29]。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003c/em\u003e","title":"基于人脸特征点的驾驶员疲劳检测算法研究","author":"徐妙语","comeFrom":"博硕","percentage":67.6303,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"06","organ":"河南大学","textid":"49ed53c8f533840d3c3b01111abb4766","category":"CMFD","context":"到的。这些特征以不同类型不同形式组合成不同的\n特征模板。特征模板内有白色和黑色两种矩形，定义该模板的\u003cem\u003e特征值为白色矩形像素和\n减去黑色矩形像素和。\nHaar-like 特征值反映了图像的灰度变化情况。将上面的任意一个矩形\u003c/em\u003e放到人脸区域\n上，然后，将白色区域的像素和减去黑色区域的像素和，得到的值称之为人脸特征值。\n","copyCount":1.0,"copyKeywords":"疲劳检测;人脸检测;人脸特征点;模糊神经网络","startIndex":14865.0,"endIndex":14908.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"},{"id":3.2382688E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度\u003cbr/\u003e特征值定义为白色矩形像素灰度和减去黑色矩形像素灰度\n和","returnContent":"选取少量特征构成强分类器； \n（3）将强分类器串联成级联分类器； \n（4）易于计算机实现； \n（5）计算速度快； \n5.1 Haar-like 特征 \n5.1.1 Haar-like 特征选择\n称矩形特征，最初由Papageorgiiou提出，因类似于haar小波\n而得名[38]。它对一些简单的图形结构比较敏感，如边缘、线段等，脸部的一些\n特征\n色矩形像素灰度和减去中间黑\n[36]\n \nHaar-like特征也\n能够由矩形特征简单地描述，因此可以在一定程度上区分人脸和非人脸。 \n矩形特征的特征值定义为图像中两个或者多个形状大小相同的矩形内，所有白\n色矩形像素灰度和与黑色矩形像素灰度和的差值，如图 5-1所示矩形特征，a)、\nb)为双矩形特征，其特征值定义为白色矩形像素灰度和减去黑色矩形像素灰度\n和。c)为三矩形特征，其特征值定义为两边的白\n哈尔滨工业大学工学硕士学位论文 \n - 39 -\n色矩形像素灰度和。d)为四矩形特征，其特征值定义为对角线上白色矩形像素\n灰度和减去对\n \n图 5-1 Haar-like 特征 \nFig. 5-1 Haar-like features \n可以利用这些矩形特征，对人脸图像的局部特征进行匹配，从而找到人脸\n图像的一些特征值。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。\u003c/em\u003e","title":"基于DM6446的嵌入式近红外人脸识别系统","author":"邵天双","comeFrom":"博硕","percentage":55.7908,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2010","num":"02","organ":"哈尔滨工业大学","textid":"5c8e549da5d98333948851932011632b","category":"CMFD","context":"\n色矩形像素灰度和与黑色矩形像素灰度和的差值，如图 5-1所示矩形特征，a)、\nb)为双矩形特征，其\u003cem\u003e特征值定义为白色矩形像素灰度和减去黑色矩形像素灰度\n和\u003c/em\u003e。c)为三矩形特征，其特征值定义为两边的白\n哈尔滨工业大学工学硕士学位论文 \n - 39 -","copyCount":1.0,"copyKeywords":"DSP;达芬奇技术;人脸检测;人脸识别","startIndex":14865.0,"endIndex":14906.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"},{"id":3.2382687E7,"fileName":"2019052108283143859751.doc","checkContent":"特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像\u003cbr/\u003e特征值为两个白色矩形像素和与两个黑色矩形像素和\n的差，反映了图像","returnContent":"另外，利用基于\n像素的特征进行检测时，需要对目标进行多尺度金字塔式扫描[26]，运算量大，\n检测效率低。因此，Paul Viola 于 2001 年提出了 Haar-like 特征[27]。\nHaar-like 特征又称为矩形特征，描述了矩形之间的灰度差异。对于一幅 20\n×20 的图像来说，矩形特征的数量超过 78,000 个。如何计算特征值及在众多特\n征中找到有效的特征是本节介绍的重点。\n8\nLienHart 和 Maydt[28]对矩形特征挑选算法进行了完善，形成三大类 Haar 特\n征模板，分别为边缘特征，线性特征和中心环绕特征，如图 2.1 所示。前两个特\n征是边缘特征，又称作 2-矩形特征，由两个相同的矩形特征相邻或垂直组成，\n特征值为白色矩形像素与黑色矩形像素的差，反映了边缘信息。紧接着的两个矩\n形特征是 3-矩形特征，由 3 个相同的矩形相邻或垂直组成，特征值为两个白色\n矩形像素之和与黑色矩形像素的差，反映了线性信息。最后的特征为中心边缘特\n征，又称为 4-矩形特征，特征值为两个白色矩形像素和与两个黑色矩形像素和\n的差，反映了图像上中心和周围的差异。\n图 2.1 基本矩形特征模板\n矩形特征对结构的描述比较粗略，仅对一些例如边缘、线段等简单的、具有\n特定走向的图形结构比较敏感有效。","keyWordsShow":"\u003cem\u003e特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像\u003c/em\u003e","title":"基于AdaBoost算法的人脸检测研究","author":"张九蕊","comeFrom":"博硕","percentage":69.1662,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2012","num":"10","organ":"兰州理工大学","textid":"9d436bcabbd70f9e2e335a96ed06f507","category":"CMFD","context":"\n矩形像素之和与黑色矩形像素的差，反映了线性信息。最后的特征为中心边缘特\n征，又称为 4-矩形特征，\u003cem\u003e特征值为两个白色矩形像素和与两个黑色矩形像素和\n的差，反映了图像\u003c/em\u003e上中心和周围的差异。\n图 2.1 基本矩形特征模板\n矩形特征对结构的描述比较粗略，仅对一些例","copyCount":1.0,"copyKeywords":"人脸检测;AdaBoost算法;粒子群优化算法;适应度函数;最小类方差","startIndex":14865.0,"endIndex":14897.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"},{"id":3.2382824E7,"fileName":"2019052108283143859751.doc","checkContent":"矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003cbr/\u003e矩形像素和，s2为黑色矩形像素和。图像的灰度变化\n情况","returnContent":"模板中有白、黑两种类型的矩\n形，前者的像素和减去后者的像素和就是模板的特征值[43]。其定义如式2-1所示，\n其中x为输入图像，s1为白色矩形像素和，s2为黑色矩形像素和。图像的灰度变化\n情况可以由这种Haar特征值反映出来，即矩形特征能够描述脸部的一些特点，比\n如脸颊的颜色明显要比眼睛部位浅，而嘴巴则明显比嘴唇周边的颜色要深等。但\n矩形特征只能描述水平、垂直或对角等特定走向的结构，因为它们只对边缘、线\n段等简单的图形结构敏感。 \n fj(x) (s1)j (s2)j (2-1) \n-16- \n哈尔滨工业大学工程硕士学位论文 \n在图2-9中，对于图中的(a)、(b)和(e)这类特征，特征数值为白色矩形像素和\n减去黑色矩形像素和，而对于(c)和(d)来说，为了使两种矩形区域中像素数目一致，\n计算公式方法为白色矩形像素和减去两倍黑色矩形像素和。 \n2.3.2.2 LBP特征 \nLBP[44]特征于1994年由Ojala M.P和D. Harwood提出，是描述图像局部纹理\n特征的一种算子。由于其旋转不变性和灰度不变性等优点特别适用于提取图像的\n局部的纹理特征。原始的LBP算子定义在3*3的窗口内，经过研究人员的不断优\n化和改进，出现了各种新形式，其中最出名的当属圆形LBP算子。","keyWordsShow":"\u003cem\u003e就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003c/em\u003e","title":"基于RLAB特征的人脸在线检测系统设计与实现","author":"吴忠谦","comeFrom":"博硕","percentage":57.4293,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"02","organ":"哈尔滨工业大学","textid":"d3054a3898b58c99da53cdb7d7f1e318","category":"CMFD","context":"像素和减去后者的像素和就是模板的特征值[43]。其定义如式2-1所示，\n其中x为输入图像，s1为白色\u003cem\u003e矩形像素和，s2为黑色矩形像素和。图像的灰度变化\n情况\u003c/em\u003e可以由这种Haar特征值反映出来，即矩形特征能够描述脸部的一些特点，比\n如脸颊的颜色明显要比","copyCount":1.0,"copyKeywords":"人脸检测;连续Adaboost;RLAB特征;I/O完成端口","startIndex":14868.0,"endIndex":14908.0,"checkid":1.33210821E8,"isQueto":0.0,"md5Code":"92958818c469bdc785290547c2a3b0fb"},{"id":3.2382684E7,"fileName":"2019052108283143859751.doc","checkContent":"矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003cbr/\u003e矩形像素和，s2为黑色矩形像素和。图像的灰度变化\n情况","returnContent":"模板中有白、黑两种类型的矩\n形，前者的像素和减去后者的像素和就是模板的特征值[43]。其定义如式2-1所示，\n其中x为输入图像，s1为白色矩形像素和，s2为黑色矩形像素和。图像的灰度变化\n情况可以由这种Haar特征值反映出来，即矩形特征能够描述脸部的一些特点，比\n如脸颊的颜色明显要比眼睛部位浅，而嘴巴则明显比嘴唇周边的颜色要深等。但\n矩形特征只能描述水平、垂直或对角等特定走向的结构，因为它们只对边缘、线\n段等简单的图形结构敏感。 \n fj(x) (s1)j (s2)j (2-1) \n-16- \n哈尔滨工业大学工程硕士学位论文 \n在图2-9中，对于图中的(a)、(b)和(e)这类特征，特征数值为白色矩形像素和\n减去黑色矩形像素和，而对于(c)和(d)来说，为了使两种矩形区域中像素数目一致，\n计算公式方法为白色矩形像素和减去两倍黑色矩形像素和。 \n2.3.2.2 LBP特征 \nLBP[44]特征于1994年由Ojala M.P和D. Harwood提出，是描述图像局部纹理\n特征的一种算子。由于其旋转不变性和灰度不变性等优点特别适用于提取图像的\n局部的纹理特征。原始的LBP算子定义在3*3的窗口内，经过研究人员的不断优\n化和改进，出现了各种新形式，其中最出名的当属圆形LBP算子。","keyWordsShow":"\u003cem\u003e就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形\u003c/em\u003e","title":"基于RLAB特征的人脸在线检测系统设计与实现","author":"吴忠谦","comeFrom":"博硕","percentage":57.4293,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"02","organ":"哈尔滨工业大学","textid":"d3054a3898b58c99da53cdb7d7f1e318","category":"CMFD","context":"像素和减去后者的像素和就是模板的特征值[43]。其定义如式2-1所示，\n其中x为输入图像，s1为白色\u003cem\u003e矩形像素和，s2为黑色矩形像素和。图像的灰度变化\n情况\u003c/em\u003e可以由这种Haar特征值反映出来，即矩形特征能够描述脸部的一些特点，比\n如脸颊的颜色明显要比","copyCount":1.0,"copyKeywords":"人脸检测;连续Adaboost;RLAB特征;I/O完成端口","startIndex":14868.0,"endIndex":14908.0,"checkid":1.33210822E8,"isQueto":0.0,"md5Code":"ef25effab916928c3cf99b14b003ee2e"}],"checkContent":"矩形特征、四矩形特征。这种特征是使用黑白两色的矩形来进行描述的，特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征的缺点就是只能对一些","endIndex":14920.0,"checkContentShow":"，这是一种基于块的特征[11]。一般可以分为三种：两矩形特征、三\u003cem\u003e矩形特征、四矩形特征。这种特征是使用黑白两色的矩形来进行描述的，特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征的缺点就是只能对一些\u003c/em\u003e比较简单的图像进行描述。\n因此，对比看来，我们选用HOG特征来作","chapterNum":0.0,"index":35},"12918":{"num":27.0,"wordNum":40.0,"startIndex":12918.0,"color":"red","copyRate":63.7183,"yyIndexs":[],"list":[{"id":3.2382876E7,"fileName":"2019052108283143859751.doc","checkContent":"执行流程如图3.1所示。\n图3.1 图像人数检测软件的软件执行流程\u003cbr/\u003e执行流程如图 6-3 所示。\n56\n图6-3 路由软件执行流程","returnContent":"调用 SDWML 网络层的转发函数接口为无线终端节点进行数据路径转发。其程\n序执行流程如图 6-3 所示。\n56\n图6-3 路由软件执行流程\n这里以路由节点的软件实现为例，按照图 6-3 的软件执行流程，利用第四章中\n设计的 SDWML 中间件软件驱动对应的图标控件进行编程。图 6-4 显示了利用\nSDWML 图形化应用开发平台编写路由节点程序的实例。路由节点先利用\n“NLMEJoinPAN”图标控件对应的“加入 PAN 网络”函数加入 PAN 网络中，之后\n一直等待接收数据，利用“Calculator”图表控件判断是命令帧还是数据帧，最后分\n别调用“NLDEDataPKTProcess”和“NLDECmdPKTProcess”图标控件调用对应网\n络层函数来处理数据帧和命令帧，一个执行流程结束。由图 6-4 可看出，将每个函\n数流程看成了图标控件编程，逻辑清晰，方便快捷。且其对应的代码会在图形化应\n用开发平台代码区域自动生成，这样便很好地统一了应用层的程序框架，简化了\nWSN 的应用开发难度，降低了应用门槛。\n图6-4 路由节点软件图形化平台开发实例\n图 6-5 为 SDWML 图形化应用平台下载路由节点代码效果界面。","keyWordsShow":"\u003cem\u003e检测软件的软件执行流程如图3.1所示。\n图3.1 图像人数检测软件的软件执行流程\u003c/em\u003e","title":"基于MC12311微控制器WSN中间件的设计研究及应用","author":"石晶","comeFrom":"博硕","percentage":63.7183,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"11","organ":"苏州大学","textid":"1fd6ef5e2f4499db1baf81d449b0eb2f","category":"CMFD","context":"调用 SDWML 网络层的转发函数接口为无线终端节点进行数据路径转发。其程\n序\u003cem\u003e执行流程如图 6-3 所示。\n56\n图6-3 路由软件执行流程\u003c/em\u003e\n这里以路由节点的软件实现为例，按照图 6-3 的软件执行流程，利用第四章中\n设计的 SDW","copyCount":5.0,"copyKeywords":"WSN;MC12311;软件构件;硬件构件;中间件;图形化","startIndex":12918.0,"endIndex":12958.0,"checkid":1.33210785E8,"isQueto":0.0,"md5Code":"d739fb9f834164f72258f573a929ceb2"},{"id":3.2382875E7,"fileName":"2019052108283143859751.doc","checkContent":"软件的软件执行流程如图3.1所示。\n图3.1 图像人数检测软件的软件执行流程\n首先\u003cbr/\u003e软件系统执行流程图如图 4.1 所示。\n图 4.1 软件执行流程图\n上电后，首先","returnContent":"1 系统软件流程\n车道偏离预警系统的软件系统执行流程图如图 4.1 所示。\n图 4.1 软件执行流程图\n上电后，首先运行在 ARM Cortex-A9 处理器上的嵌入式 Linux 系统首先完成初始化\n并启动系统，然后将 FPGA 的相关配置信息及 Flash 中的程序配置入 FPGA，并完成车辆\n安装摄像机等其他系统部件的初始化。系统软件流程如下：首先完成车辆安装的摄像机\n及其他系统部件的初始化。当软件系统按照流程图 4.1 所示流程完成系统初始化后，系\n统进入车道偏离检测及预警判定流程。这时启动车辆前方安装的摄像机开始图像信息采\n集，并调用 FPGA 内部图像处理模块对图像信息进行颜色空间转换、灰度化、中值滤波、\n边缘检测、车道线检测等图像处理流程。\n程序首先加载经边缘检测的图像信息，然后将图像分为左右两部分进行直线检测。\n通常情况在实际应用中，车道接近水平或垂直的概率是非常小的，同时为了过滤干扰信\n 10\n \n息(如路边灯杆、电线杆、地平线、前方车辆边缘等)，在使用 Hough 变换进行图像信息\n中直线检测的过程中采取如下方法：首先将图像分为两部分。在左半部图像中，方向角\n在 95°~175°之间进行直线检测；在右半部图像中，方向角在 5°~85°之间进行直线\n检测。","keyWordsShow":"\u003cem\u003e软件的软件执行流程如图3.1所示。\n图3.1 图像人数检测软件的软件执行流程\n首先\u003c/em\u003e","title":"嵌入式车道偏离预警系统","author":"韩博","comeFrom":"博硕","percentage":54.9126,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2014","num":"01","organ":"东北师范大学","textid":"dcc9b8be345720fedcaf63919023dc23","category":"CMFD","context":"1 系统软件流程\n车道偏离预警系统的\u003cem\u003e软件系统执行流程图如图 4.1 所示。\n图 4.1 软件执行流程图\n上电后，首先\u003c/em\u003e运行在 ARM Cortex-A9 处理器上的嵌入式 Linux 系统首先完成初始化\n并启动","copyCount":6.0,"copyKeywords":"车道偏离;嵌入式;车道检测","startIndex":12920.0,"endIndex":12961.0,"checkid":1.33210785E8,"isQueto":0.0,"md5Code":"d739fb9f834164f72258f573a929ceb2"}],"checkContent":"检测软件的软件执行流程如图3.1所示。\n图3.1 图像人数检测软件的软件执行流程\n首先","endIndex":12961.0,"checkContentShow":"\u003cem\u003e检测软件的软件执行流程如图3.1所示。\n图3.1 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下面出贝叶斯定理:\nP〔B}且)\n刃(‘改】B)p(B)\n P(八)\n(4)\n 3.朴素贝叶斯分类的原理与流程\n 朴素贝叶斯分类是一种十分简单的分类算法，叫它朴素贝叶斯分类是因为这种\n方法的思想真的很朴素，朴素贝叶斯的思想基础是这样的:对于给出的待分类项，\n求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属\n于哪个类别。在没有其它可用信息下，我们会选择条件概率最大的类别，这就是\n朴素贝叶斯的思想基础。\n 朴素贝叶斯分类的正式定义如下:\n 设，、\u003d{“，·以2一‘;，;;}为一个待分类项，而每个。为x的一个特征属性，有类\n别集合‘’二{.111·处…热;}，计算尸(，，j:}.0.尸(央卜)…尹(幼，I，，4)，如果\n尸(，f/、{.1.)二，，、以尸(，i/:}，门.尸(.ljZ卜)‘二』p(仄。}川}，则:任纷、。\n 从定义可以发现关键在于如何计算各个条件概率。做法如下:\n山东大学硕十学位论文\n (l)找到一个已知分类的待分类项集合，这个集合叫做训练样本集。\n (2)统计得到在各类别下各个特征属性的条件概率估计。","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴\u003cspan class\u003d\"yellow\"\u003e素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]\u003c/span\u003e。这个\u003c/em\u003e","title":"基于MVC的人力资源管理系统的设计与实现","author":"孙耀","comeFrom":"博硕","percentage":65.8007,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2011","num":"07","organ":"山东大学","textid":"40b0ea4db6fed0b6527227164cbdca72","category":"CMFD","context":"B}且)\n刃(‘改】B)p(B)\n P(八)\n(4)\n 3.朴素贝叶斯分类的原理与流程\n 朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法，叫它朴素贝叶斯分类是因为这种\n方法的思想真的很朴素，朴素贝叶斯的思想基础是这样的:对于给出的待分类项，\n求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属\n于哪个类别\u003c/em\u003e。在没有其它可用信息下，我们会选择条件概率最大的类别，这就是\n朴素贝叶斯的思想基础。\n 朴素","copyCount":3.0,"copyKeywords":"人力资源管理;MVC设计模式;统一建模语言;贝叶斯分类器","startIndex":13796.0,"endIndex":13922.0,"checkid":1.33210804E8,"isQueto":1.0,"md5Code":"6a3ab269b757c21aa4b41b4a2a9cd0dc"},{"id":3.2382804E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003cbr/\u003e分类是一种十分简单的分类算法，朴素贝叶斯的思想基础是这样的：对于给出\n的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项","returnContent":"得到全局最优化，并且在整个样本空间的期望风险以某个概率满足一定上界。\n在协议识别和网络流量分类中,SVM具有良好的稳定性和较高的分类准确率。\n(3)朴素贝叶斯\n贝叶斯分类是一类分类算法的总称，这类算法均以贝叶斯定理为基础，故统称为贝叶斯分\n类。如果设P(H|X)表示条件X下H的后验概率。P(H)是表示H发生的先验概率。则贝叶斯\n定理是：\nP(H 丨 X)\u003dP(X|H)P(H)/P(X)。 (2.4)\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想基础是这样的：对于给出\n的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\n12\n南京邮电大学硕士研究生学位论文协议识别相关技术3\n ：第二章 属于哪个类别。朴素贝叶斯分类器依靠精确的自然概率模型，在有监督学习的样本集中能获\n取得非常好的分类效果。在许多实际应用中，朴素贝叶斯模型参数估计使用最大似然估计方\n法，朴素贝叶斯分类器的一个优势在于只需要根据少量的训练数据估计出必要的参数（变量\n的均值和方差）。\n2. 3. 3无监督机器学习\n无监督机器学习是指训练数据集中的数据都是未标记的，它主要使用一个内在启发式的\n方法来对训练数据进行分类，形成多个簇，同一簇中的数据都有明显的相似性，不同簇中的\n数据则有很大的相异性。","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003c/em\u003e","title":"基于流统计特性的应用协议识别技术研究","author":"李宁","comeFrom":"博硕","percentage":60.3564,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"05","organ":"南京邮电大学","textid":"fbadbb2263ea1446cdfb0d4ee0691795","category":"CMFD","context":"概率。则贝叶斯\n定理是：\nP(H 丨 X)\u003dP(X|H)P(H)/P(X)。 (2.4)\n朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法，朴素贝叶斯的思想基础是这样的：对于给出\n的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003c/em\u003e\n12\n南京邮电大学硕士研究生学位论文协议识别相关技术3\n ：第二章 属于哪个类别。朴素贝叶","copyCount":3.0,"copyKeywords":"协议识别;流;统计;特征选择;HCM算法;K-L距离","startIndex":13796.0,"endIndex":13909.0,"checkid":1.33210804E8,"isQueto":0.0,"md5Code":"6a3ab269b757c21aa4b41b4a2a9cd0dc"},{"id":3.2383825E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此\u003cbr/\u003e分类是一种十分简单的分类算法,朴素贝叶斯的思想基础是:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率,哪个最大,就认为此","returnContent":"2017年4月26日-朴素贝叶斯分类是一种十分简单的分类算法,朴素贝叶斯的思想基础是:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率,哪个最大,就认为此...","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此\u003c/em\u003e","title":"DMP核心流程-样本训练【技术类】-简书","comeFrom":"互联网","percentage":56.525,"type":"red","category":"NET","context":"2017年4月26日-朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法,朴素贝叶斯的思想基础是:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率,哪个最大,就认为此\u003c/em\u003e...","copyCount":3.0,"startIndex":13796.0,"endIndex":13905.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.2382808E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为\u003cbr/\u003e朴素贝叶斯分类算法是一种很简单的分类思想，对于给出的分类项，求\n解在此项出现的条件下各个类别出现的概率，哪个最大就认为","returnContent":"- 15 -\n哈尔滨理工大学工学硕士学位论文\n字符串的特征。\n新 浪 微 博\n-48 -62 -64 -53 -50 -94 -78 -87\n-48 -62 -64 -53\n-62 -64 -53 -50\n-64 -53 -50 -94\n-53 -50 -94 -78\n-50 -94 -78 -87\n图3-1 n-grams提取中文微博特征\nFig.3-1 The features of Chinese microblog are extracted with n-grams method\n3.2 机器学习算法\n3.2.1 朴素贝叶斯过滤算法\n朴素贝叶斯分类算法是一种很简单的分类思想，对于给出的分类项，求\n解在此项出现的条件下各个类别出现的概率，哪个最大就认为该分类项属于\n哪个类别[39]。\n朴素贝叶斯分类定义如下：\n设x \u003d {α 1 , α 2,..., αm}为一个待分类项，而每个 α 为 x 的一个特征属性。\n有类型集合C \u003d { y1 , y 2,... yn}，其中每个 y 为一个类别。然后分别计算\nP ( y1 | x)，P ( y 2| x)，...，P ( y n| x )。","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为\u003c/em\u003e","title":"社会网络平台中的垃圾信息过滤技术研究","author":"杨明明","comeFrom":"博硕","percentage":50.6162,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"05","organ":"哈尔滨理工大学","textid":"944451e5271644548fd7739f88bb6370","category":"CMFD","context":"ed with n-grams method\n3.2 机器学习算法\n3.2.1 朴素贝叶斯过滤算法\n\u003cem\u003e朴素贝叶斯分类算法是一种很简单的分类思想，对于给出的分类项，求\n解在此项出现的条件下各个类别出现的概率，哪个最大就认为\u003c/em\u003e该分类项属于\n哪个类别[39]。\n朴素贝叶斯分类定义如下：\n设x \u003d {α 1 , α 2,","copyCount":3.0,"copyKeywords":"垃圾信息过滤;机器学习;特征选择;增量式模型;社会网络","startIndex":13796.0,"endIndex":13904.0,"checkid":1.33210804E8,"isQueto":0.0,"md5Code":"6a3ab269b757c21aa4b41b4a2a9cd0dc"},{"id":3.238383E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现\u003cbr/\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现","returnContent":"2016年9月14日-朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现的条...","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现\u003c/em\u003e","title":"【干货】机器学习,你需要知道的十个算法","comeFrom":"互联网","percentage":61.7283,"type":"red","category":"NET","context":"2016年9月14日-朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现\u003c/em\u003e的条...","copyCount":3.0,"startIndex":13796.0,"endIndex":13882.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.2383823E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解\u003cbr/\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解","returnContent":"2017年12月27日-朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解...","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解\u003c/em\u003e","title":"朴素贝叶斯分类(NaveBayes)-郭云飞的专栏-CSDN博客","comeFrom":"互联网","percentage":60.0881,"type":"red","category":"NET","context":"2017年12月27日-朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解\u003c/em\u003e...","copyCount":3.0,"startIndex":13796.0,"endIndex":13877.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.2383826E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待\u003cbr/\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待","returnContent":"2015年8月2日-     朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待...","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，\u003c/em\u003e","title":"朴素贝叶斯分类算法(NaiveBayesianclassification)-..._CSDN博客","comeFrom":"互联网","percentage":58.4938,"type":"red","category":"NET","context":"2015年8月2日-     朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待\u003c/em\u003e...","copyCount":3.0,"startIndex":13796.0,"endIndex":13875.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.2383828E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项\u003cbr/\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思...想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项","returnContent":"2015年9月9日-朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思...想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,...","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项\u003c/em\u003e","title":"朴素贝叶斯分类_百度文库","comeFrom":"互联网","percentage":59.4405,"type":"red","category":"NET","context":"2015年9月9日-朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思...想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类项\u003c/em\u003e,...","copyCount":3.0,"startIndex":13796.0,"endIndex":13874.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.2383829E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类\u003cbr/\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类","returnContent":"2013年5月23日-求翻译:朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类...","keyWordsShow":"\u003cem\u003e于数据挖掘的分类算法有很多种，有以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类\u003c/em\u003e","title":"朴素贝叶斯分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是...","comeFrom":"互联网","percentage":58.7652,"type":"red","category":"NET","context":"2013年5月23日-求翻译:朴素贝叶斯\u003cem\u003e分类是一种十分简单的分类算法,叫它朴素贝叶斯分类是因为这种方法的思想真的很朴素,朴素贝叶斯的思想基础是这样的:对于给出的待分类\u003c/em\u003e...","copyCount":3.0,"startIndex":13796.0,"endIndex":13873.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.2382806E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]。这个算法\u003cbr/\u003e分类。朴素贝叶斯分类是一种\n十分简单的分类算法，对于给出的待分类项，求解在此项出现的条件下各个类\n别出现的概率，哪个最大，就认为此待分类项属于哪个类别。贝叶斯算法","returnContent":"通过以上分析得到朴素贝叶斯分类算法以及决策树 ID3 分类法的基本构\n建流程，通过 Java Script 构建算法基础。贝叶斯分类是一类分类算法的总称，这\n类算法均以贝叶斯定理为基础，故统称为贝叶斯分类。朴素贝叶斯分类是一种\n十分简单的分类算法，对于给出的待分类项，求解在此项出现的条件下各个类\n别出现的概率，哪个最大，就认为此待分类项属于哪个类别。贝叶斯算法实现\n流程图如图 4-16 所示。第四章 代码智能发布系统详细设计与实现\n38\n图 4-16 朴素贝叶斯算法流程\n朴素贝叶斯运用过程中分为三个阶段：\ni. 准备工作阶段，这部分工作是为了朴素贝叶斯分类做准备，工作内\n容主要是根据实际情况筛选分类属性，对比区分所有分类属性，此\n时需要人为操作对其中无法识别的属性做分类，构建分类训练样本\n集合。此时的所有数据输入属于所有待分类数据项，输出则是特征\n属性以及训练样本数据。这是整个朴素贝叶斯分类算法实现中唯一\n需要人工介入完成的阶段，其样本数据训练的质量将影响整个分类\n过程。\nii. 分类器训练阶段，这部分工作任务是分类器的生成，其工作内容是\n计算数据集合每个类别在训练样本中出现的频率以及每个特征属性\n区分对每个类别的概率，并统计输出结果。","keyWordsShow":"\u003cem\u003e以下常见的分类算法。\n（1）朴素贝叶斯\n朴\u003cspan class\u003d\"yellow\"\u003e素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]\u003c/span\u003e。这个算法\u003c/em\u003e","title":"代码智能发布系统的设计与实现","author":"吴奇钧","comeFrom":"博硕","percentage":68.7205,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"04","organ":"中国科学院大学(中国科学院工程管理与信息技术学院)","textid":"75db96936e38d8a19132e00aa1c8e79f","category":"CMFD","context":"ipt 构建算法基础。贝叶斯分类是一类分类算法的总称，这\n类算法均以贝叶斯定理为基础，故统称为贝叶斯\u003cem\u003e分类。朴素贝叶斯分类是一种\n十分简单的分类算法，对于给出的待分类项，求解在此项出现的条件下各个类\n别出现的概率，哪个最大，就认为此待分类项属于哪个类别。贝叶斯算法\u003c/em\u003e实现\n流程图如图 4-16 所示。第四章 代码智能发布系统详细设计与实现\n38\n图 4-16","copyCount":3.0,"copyKeywords":"代码智能发布;部署策略;灰度发布;web技术;可视化","startIndex":13812.0,"endIndex":13924.0,"checkid":1.33210804E8,"isQueto":1.0,"md5Code":"6a3ab269b757c21aa4b41b4a2a9cd0dc"},{"id":3.2382802E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]。这个\u003cbr/\u003e朴素贝叶斯分类是一种十分简单的分类算^^。朴素贝叶斯的\n思想基础是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出\n现的概率，哪个最大，就认为此待分类项属于哪个类别","returnContent":"则公式2-1中的X可以理解为“具有某特征”，而7\n可以理解为“某标签”[13]。则对于二分类问题而言，最终目的就是要判断\n尸(“属于某类具有某特征”）是否大于1/2。贝叶斯方法把计算“具有某特征的\n条件下属于某类”的概率转换成需要计算“属于某类的条件下具有某特征”的概\n率，而后者获取方法很简单，找到一些包含已知特征标签的样本，即可进行训练。\n在中文中，句子是由字构成而不是词构成，所以为了更好的对句子进行表征，\n在处理之前需要对句子进行分词。汉语中常用词的个数不到80000个，而由常用\n词组成的句子是无限多的，所以中文自然语言处理大多以词为单位进行。\n在贝叶斯公式的基础上，加上条件独立假设的贝叶斯方法就是朴素贝叶斯方\n法（Naive Bayes),朴素贝叶斯分类是一种十分简单的分类算^^。朴素贝叶斯的\n思想基础是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出\n现的概率，哪个最大，就认为此待分类项属于哪个类别。有些独立假设在各个分\n类之间的分布都是均匀的所以对于似然的相对小不产生影响；即便不是如此，\n也有很大的可能性各个独立假设所产生的消极影响或积极影响互相抵消，最终导\n致结果受到的影响不大[14]。","keyWordsShow":"\u003cem\u003e以下常见的分类算法。\n（1）朴素贝叶斯\n朴\u003cspan class\u003d\"yellow\"\u003e素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]\u003c/span\u003e。这个\u003c/em\u003e","title":"海量短信数据中异常行为的研究","author":"湛然","comeFrom":"博硕","percentage":72.4054,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2018","num":"10","organ":"北京邮电大学","textid":"cb9140de01c53955685f70878cd6a188","category":"CMFD","context":"\n在贝叶斯公式的基础上，加上条件独立假设的贝叶斯方法就是朴素贝叶斯方\n法（Naive Bayes),\u003cem\u003e朴素贝叶斯分类是一种十分简单的分类算^^。朴素贝叶斯的\n思想基础是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出\n现的概率，哪个最大，就认为此待分类项属于哪个类别\u003c/em\u003e。有些独立假设在各个分\n类之间的分布都是均匀的所以对于似然的相对小不产生影响；即便不是如此，","copyCount":3.0,"copyKeywords":"文本分类;垃圾短信;特征提取;电信诈骗","startIndex":13812.0,"endIndex":13922.0,"checkid":1.33210804E8,"isQueto":1.0,"md5Code":"6a3ab269b757c21aa4b41b4a2a9cd0dc"},{"id":3.2383832E7,"fileName":"2019052108283143859751.doc","checkContent":"分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003cbr/\u003e一种十分简单的分类算法,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率","returnContent":"2015年1月8日-     朴素贝叶斯分类是一种十分简单的分类算法,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率,...","keyWordsShow":"\u003cem\u003e以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003c/em\u003e","title":"大数据分类算法——朴素贝叶斯算法-没有什么不可能-CSDN博客","comeFrom":"互联网","percentage":59.5674,"type":"red","category":"NET","context":"2015年1月8日-     朴素贝叶斯分类是\u003cem\u003e一种十分简单的分类算法,朴素贝叶斯的思想基础是这样的:对于给出的待分类项,求解在此项出现的条件下各个类别出现的概率\u003c/em\u003e,...","copyCount":3.0,"startIndex":13812.0,"endIndex":13909.0,"checkid":1.33220858E8,"isQueto":0.0,"md5Code":"d54b3a116236c0952231fd4bd1cf3a1d"},{"id":3.238281E7,"fileName":"2019052108283143859751.doc","checkContent":"常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003cbr/\u003e分类算法就成为朴素的，\n即朴素贝叶斯算法。基本原理是：对于给定的待分类项\n （4.10）\n其中， 为 Q 的一个特征属性。求解在此待分类项出现的条件下各个类别 会出现\n的概率，哪个 最大，就","returnContent":"值判断其属性类别 B 的概率，称为后验概长安大学硕士学位论文\n40\n率。 是直接判断某个样本属于 B 的概率，称为先验概率。 是在类别 B 中观\n测到 A 得概率， 是在数据库中观测到 A 的概率。\n （4.9）\n2. 朴素贝叶斯分类算法原理\n假设待分类项的各个属性相互独立的情况下，构造出来的分类算法就成为朴素的，\n即朴素贝叶斯算法。基本原理是：对于给定的待分类项\n （4.10）\n其中， 为 Q 的一个特征属性。求解在此待分类项出现的条件下各个类别 会出现\n的概率，哪个 最大，就把待分类文件归属于哪个类。\n其实现步骤为：\n1）设 为一个待分类项， 为 Q 的一个特征属性。\n2）有类别集合 。\n3）计算 ， ， ， ；\n其中：\n （4.11）\n分母即数据库中 Q 存在的概率，所以对于任意一个待分类项来说 都是常数固\n定的。求后验概率 只要考虑分子即可。因为各特征值是独立的，所以\n （4.12）\n对于 是指在训练样本中 出现的概率，可以近似的求解为：\n （4.13）\n对于先验概率 ，是指类别 中，特征元素 出现的概率，公式为\n 在训练样本为 时， 出现的次数\n 训练样本数 （4.","keyWordsShow":"\u003cem\u003e以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项\u003c/em\u003e","title":"云分类学术搜索引擎的研究与实现","author":"曾盼盼","comeFrom":"博硕","percentage":55.6949,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"03","organ":"长安大学","textid":"1502d342ea108ced4913d3ff89e6c5e2","category":"CMFD","context":"率。\n 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*/DF权重值就很大，该词组在判断文档相似\n性的过程中起到的作用也就比较大。\n2.4朴素贝叶斯分类算法\n由于资讯类站点中包含数量庞大的文章集合，计算每两个文章间的相似度的\n时间复杂度非常高。为了提高算法的运行效率，降低文本相似性计算的时间复杂\n度，需要通过文本分类对文本相似性计算进行降维。通过文本分类，将文章集合\n划分为不同的分类，相似度计算过程中只对同一类别中的文章进行计算。因而，\n算法需要对输入文本首先进行分类处理。常用的文本分类算法有K-NN、朴素贝叶\n斯、支持向量机和最大熵算法[26]。朴素贝叶斯分类算法具有实现简单，分类和训\n练速度都很快的特点[27】，因而选择该算法对文本进行分类。\n朴素贝叶斯分类算法是一种相对来说比较简单的分类算法。其基于的思想是：\n13\n北京交通大学专业硕士学位论文相关理论概述\n 对于给定的待分类项，求解在此项出现的条件下各个类别出现的概率，最大的那\n个就认为此待分类项属于该类别。其根据如下的贝叶斯公式中体现的理论基础来\n求解对于待分类项Z属于各个类别的概率：\nPix\\OPC|(cP()\n_Y) 一 P⑷ 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Bayes)，一种简\n单的贝叶斯分类算法","returnContent":"而贝叶斯分类就是一种能够有效利用已有的样\n本信息，并且在其学习过程中提取输入参数特征的模式识别方法[62]。\n贝叶斯分类技术在众多分类技术中占有重要地位，也属于统计学分类的范畴，是一\n种非规则的分类方法，贝叶斯分类技术通过对己分类的样本子集进行训练，学习归纳出\n分类函数（对离散变量的预测称作分类，对连续变量的分类称为回归），利用训练得到\n的分类器实现对未分类数据的分类。\n常用的贝叶斯分类方法有朴素贝叶斯分类方法和贝叶斯信念网络。朴素贝叶斯分类\n假定一个属性对分类的影响独立于其它属性的值，这一假定称为类条件独立。贝叶斯信\n念网络（贝叶斯网络或概率网络）是图形化的模型，有时人们把朴素贝叶斯看作一种特\n殊的贝叶斯网络。与朴素贝叶斯分类不同，贝叶斯网络能表示属性子集间的依赖关系，\n所以能解决具有更为复杂属性依赖关系的分类问题[63]。根据属性间依赖关系的不同，人\n们提出了不同解决算法，如LBR(Lazy Bayesian Rules，懒散贝叶斯规则）、TAN (Tree\nAugmented Naive Bayes,增广树朴素贝叶斯）和DBN(动态贝叶斯网络）等。\n通过对比分析不同的分类算法，发现朴素贝叶斯分类算法(Naive Bayes)，一种简\n单的贝叶斯分类算法，其应用效果比神经网络分类算法和判定树分类算法还要好，特别\n52\n西南石油大学硕士研究生学位论文 -\n 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Bayes，NB）算法是一种基于概率统计的分类算法。有着\n很","returnContent":"算法的这一缺点，通过调整近邻点的搜索策略，提出了一种改进型\n的KNN算法，能够有效地减少KNN算法的计算复杂度，提高分类效率。该算法将在第\n四章进行详细讨论。\n3.3 朴素贝叶斯算法\n朴素贝叶斯分类（Naive Bayes，NB）算法是一种基于概率统计的分类算法。有着\n很强的数学基础和比较稳定的分类性能。在众多的分类算法中，朴素贝叶斯算法以算\n法简单、分类精度高和实现速度快的优点而著称。\n在朴素贝叶斯算法中，一个重要的假设是样本每个特征与其他特征都不相关。虽\n然在实际情况中样本特征之间的相互依赖度可能较高或者有些特征是由另外的某些特\n征而决定的，然而朴素贝叶斯分类算法依然假设这些特征在决定样本所属类别时拥有\n者相互独立的概率分布。朴素贝叶斯分类算法发源于古典数学理论，有着坚实的数学\n基础，以及稳定的分类效率。同时，朴素贝叶斯算法所需估计的参数很少，对缺失数\n据不太敏感，算法实现简单。虽然在理论上朴素贝叶斯算法与其他分类方法相比具有\n很高的精度性能。但由于朴素贝叶斯算法假设各特征之间相互独立，所以实际应用中\n情况并不相同，朴素贝叶斯算法在几种的主流算法中精度性能不是非常突出，这给该\n算法的正确分类带来了一定影响。","keyWordsShow":"\u003cem\u003e以下常见的分类算法。\n（1）朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素\u003c/em\u003e","title":"中文网页分类算法研究","author":"钱强","comeFrom":"博硕","percentage":55.2064,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"08","organ":"江苏科技大学","textid":"4e0b8a54b1f4ffdd01d426bf3821bf45","category":"CMFD","context":"减少KNN算法的计算复杂度，提高分类效率。该算法将在第\n四章进行详细讨论。\n3.3 朴素贝叶斯算法\n\u003cem\u003e朴素贝叶斯分类（Naive Bayes，NB）算法是一种基于概率统计的分类算法。有着\n很\u003c/em\u003e强的数学基础和比较稳定的分类性能。在众多的分类算法中，朴素贝叶斯算法以算\n法简单、分类精度高","copyCount":3.0,"copyKeywords":"中文网页分类;向量空间模型;词共现图;KNN","startIndex":13812.0,"endIndex":13854.0,"checkid":1.33210803E8,"isQueto":0.0,"md5Code":"90d35671b8b2891d3a56bf4ad5127437"},{"id":3.23828E7,"fileName":"2019052108283143859751.doc","checkContent":"朴素贝叶斯\n朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]。这个\u003cbr/\u003e朴素贝叶斯算法实现。朴素贝叶斯算法是基于贝叶斯定理与特征条件独立假设的分类方法，其分类的思想基础是:对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率哪个最大，就认为此待分类\n项属于哪个类别","returnContent":"3.3 Hadoop技术应用\n3.3.1朴素贝叶斯算法实现。朴素贝叶斯算法是基于贝叶斯定理与特征条件独立假设的分类方法，其分类的思想基础是:对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率哪个最大，就认为此待分类\n项属于哪个类别|9，01。\n本实验以20NewsGroup为例，针对文档分类的具体问题，在Hadoop集群上运行Mahout中的朴素贝叶斯算法，实现对文档分类模型的训练，并测试模型分类的效果。\n20NewsGroup包含了被分成20个新闻组的20 000个新闻组文档，20个新闻组按照20个不同的类型进行组织，不同的类对应不同的主题。其中，60%用来进行训练贝叶斯分类算法，40%用来测试分类模型。具体实验流程如图4所示。\n第一，准备数据，并将数据上传至HDFS。\n//解压后得到 20news-hydate-train和 20news-hydate-test两个文件夹，上传至HDFS;\n#tar -zvxf 20news-hydate.tar.gz.\n#hdfs dfs -put 20news-hydate-train .\n#hdfs dfs -put 20news-hydate-test.","keyWordsShow":"\u003cem\u003e朴素贝叶斯\n朴\u003cspan class\u003d\"yellow\"\u003e素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]\u003c/span\u003e。这个\u003c/em\u003e","title":"Hadoop技术在云数据中心的应用研究","author":"李自尊 冯建 汤进","comeFrom":"期刊","percentage":68.994,"type":"red","periodical":"河南科技","periodicalYear":"2017","beginPage":4.0,"endPage":4.0,"num":"21","organ":"黄委会信息中心数据中心","textid":"708b076c2dcf532b8e4af9bbc0bfc786","category":"CJFD","context":"3.3 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正负样本特征合并\nhog_feature \u003d","chapterNum":0.0,"index":43},"20478":{"num":41.0,"wordNum":56.0,"startIndex":20478.0,"color":"red","copyRate":52.5774,"yyIndexs":[],"list":[{"id":3.2382718E7,"fileName":"2019052108283143859751.doc","checkContent":"每个细胞单元中的像素\ncells_per_block：表示每个块中的细胞单元\nvisualize：表示\u003cbr/\u003e每个\n细胞单元中 bin 的数量，? 表示块的个数，η 表示每个块的细胞单元","returnContent":"2）2L -Hys ；\n 方法同 1）， v 的大小最大为 0.2；\n 3）11L-norm: v (28)v/ ( v (10)?) ；\n 4）11L -sqrt: v (28)v/ ( v (10)? ；\n 在行人检测中，一般采用2L -norm 。\n （5）得到最终 HOG 特征向量\n根据以上步骤，我们最终得到 ? ?? ?η 个数据组成的高维特征向量，其中 ? 表示每个\n细胞单元中 bin 的数量，? 表示块的个数，η 表示每个块的细胞单元数量。例如：对于 40?40\n像素的图像，假设块（block）为 2?2\u003d4 个细胞单元，每个细胞单元大小是 8?8 ，则一共有\n4?4\u003d16 个块，所以该图像的 HOG 特征向量维度就是16?4?9\u003d576 。\n2.4.2 支持向量机\n90 年代中期，Cortes 和 Vapnik 根据统计学习理论知识提出了机器学习方法-支持向量机\n（Support Vector Machine，SVM）[55]，它属于监督式学习的方法，在统计分类以及回归分析\n中应用较多。通俗的讲，支持向量机是一种二分类模型，它能在训练样本不足的情况下迅速\n的学习到较好的分类决策。","keyWordsShow":"\u003cem\u003ecell：表示每个细胞单元中的像素\ncells_per_block：表示每个块中的细胞单元\nvisualize：表示\u003c/em\u003e","title":"基于区域卷积神经网络的行人检测问题研究","author":"李海龙","comeFrom":"博硕","percentage":52.5774,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"02","organ":"杭州电子科技大学","textid":"a21de47e5329d7b026e3c1f9b3d3c8b0","category":"CMFD","context":"OG 特征向量\n根据以上步骤，我们最终得到 ? ?? ?η 个数据组成的高维特征向量，其中 ? 表示\u003cem\u003e每个\n细胞单元中 bin 的数量，? 表示块的个数，η 表示每个块的细胞单元\u003c/em\u003e数量。例如：对于 40?40\n像素的图像，假设块（block）为 2?2\u003d4 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系列光栅光谱仪进行分光后，光电倍增管接收\n出射狭缝处的微弱光信号并进行多次倍增并最终转化为周期光电流。微弱的光电流经\n过第一级的 I/V 电路变换为电压信号并放大，放大后的模拟电压信号再经过二级差分\n仪表放大、二阶巴特沃斯低通滤波以及模数转换处理之后，转换为数字量电压信号，\n数字电压信号被 FPGA（可编程逻辑器件）芯片直接采样后，FPGA 芯片对采集到的信\n号在数字域中进行基于坐标旋转数字算法原理的相关解调、IIR 低通滤波等数字化信号\n处理，待 FPGA 处理完信号之后便将得到的数据发给微处理器，最后由微处理器通过\n串口将数据传输至上位机显示处理平台。整个采集系统的结构如图 2.13 所示。\n图 2.13 数据采集系统\n2.8 本章小结\n本章首先介绍了光电检测技术，根据实验室现有的条件，选择了溴钨灯作为光源，\n通过光栅型光谱仪进行分光，针对分光后的微弱出射光信号，通过对比论证选择了光\n电倍增管作为微弱光电信号的探测器件。","keyWordsShow":"\u003cem\u003e程如图2.4所示。\n2.5本章小结\n本章介绍了图像人数检测软件所需的相关技术基础，首先介绍了\u003c/em\u003e","title":"基于光电倍增管的数据采集系统设计","author":"张珮","comeFrom":"博硕","percentage":55.1033,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"02","organ":"长春理工大学","textid":"5cede453fc4e6d0989f7d9ad7a47cd61","category":"CMFD","context":"将得到的数据发给微处理器，最后由微处理器通过\n串口将数据传输至上位机显示处理平台。整个采集系统的结构\u003cem\u003e如图 2.13 所示。\n图 2.13 数据采集系统\n2.8 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等操作系统","returnContent":"公司开发的一个计算机视觉工具箱，采用 C/C++语\n言编写，可以运行在 Linux、Windows 和 Mac 等操作系统。此外，OpenCV 还提供了\nPython、Ruby、MATLAB 以及其他语言的接口，使用起来非常的方便。目前最新的\n官方版本号是 OpenCV2.4.2。\n2.2.2 OpenCV 的结构模块及内容\nOpenCV 主体分为五大模块，其中四大模块如图 2.2 所示。\n8\n第 2 章 视觉跟踪测控总体研究方案\nCVMLLHighGUI\n图像处理与视觉算法统计分类器GUI，\n图像和视频输入/输出\nCXCORE\n基本结构和算法，XML支持，绘图函数\n图 2.2 OpenCV 的基本结构\n（1）CV 模块：核心函数库，包含了各种基本的图像处理函数和高级的计算机\n视觉算法。\n（2）ML 模块：机器学习库，包含了一些基本统计的分类与聚类工具。\n（3）HighGUI 模块：GUI 函数库，包含了图像以及视频的输入、输出函数。\n（4）CXCORE 模块：数据结构与线性代数库，包含了 OpenCV 的一些基本数据\n结构以及功能定义，同时包含数据处理的相关函数。\n2.","keyWordsShow":"\u003cem\u003e一个著名的开源计算机视觉函数库，使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行\u003c/em\u003e","title":"移动机器人自主视觉跟踪测控技术研究","author":"龙忠杰","comeFrom":"博硕","percentage":62.7205,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"07","organ":"北京信息科技大学","textid":"2a6acc57319829235964b252b71a5edc","category":"CMFD","context":"公司开发的一个\u003cem\u003e计算机视觉工具箱，采用 C/C++语\n言编写，可以运行在 Linux、Windows 和 Mac 等操作系统\u003c/em\u003e。此外，OpenCV 还提供了\nPython、Ruby、MATLAB 以及其他语言的接口，使","copyCount":1.0,"copyKeywords":"移动机器人;目标跟踪;特征点提取;立体匹配;OpenCV","startIndex":14983.0,"endIndex":15040.0,"checkid":1.33210825E8,"isQueto":0.0,"md5Code":"4e9c57c0a45f69734616e8d9a3718370"},{"id":3.238278E7,"fileName":"2019052108283143859751.doc","checkContent":"使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以\u003cbr/\u003e采用 C/C++语言编写，可以再 Windows/Mac/Linux\n等操作系统上","returnContent":"OpenCV 采用 C/C++语言编写，可以再 Windows/Mac/Linux\n等操作系统上运行。OpenCV 的设计目标是可移植性高、执行速度快，注重实时\n应用。采用优化的 C 代码编写，能够充分利用多核处理器的优势。\n56\n硕士学位论文\nOpenCV 主要分为五个函数模块。OpenCV 的 CV 模块包含常用的图像处理函\n数和高级的计算机视觉算法。ML 是机器学习库，包含一些统计的分类和聚类工\n具。HighGUI 函数模块包含图像和视频输入、输出的函数。CXCore 包含 OpenCV\n的一些基本数据结构和相关函数。而 CvAux 模块中一般存放一些即将被淘汰的算\n法和函数，同时还有一些新出现的实验性的算法和函数。未来一些特性可能被合\n并到 CV 模块，还有一些可能永远留在 CvAux 中[47]。\nOpenCV 的基本结构如图 5.10 所示：\nCV\n图像处理和视MLLHighGHI\n图像和视频输\n觉算法统计分类器入输出\nCXCORE\n基本结构和算法；XML支持，绘图函数\n图 5.10 OpenCV 的基本结构图\nOpenCV 作为作为一个基本的计算机视觉、图像处理和模式识别的开源项目，\n可以直接应用于很多领域。","keyWordsShow":"\u003cem\u003e使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行。\u003c/em\u003e","title":"塑胶组合盖质量视觉检测系统研究","author":"张晓琳","comeFrom":"博硕","percentage":67.4328,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"05","organ":"湖南大学","textid":"e4c7a312a07468fc1b308b59f10f2c6a","category":"CMFD","context":"OpenCV \u003cem\u003e采用 C/C++语言编写，可以再 Windows/Mac/Linux\n等操作系统上\u003c/em\u003e运行。OpenCV 的设计目标是可移植性高、执行速度快，注重实时\n应用。采用优化的 C 代码","copyCount":1.0,"copyKeywords":"塑胶组合盖;视觉检测;圆检测;胶塞检测;成品检测","startIndex":14999.0,"endIndex":15041.0,"checkid":1.33210825E8,"isQueto":0.0,"md5Code":"4e9c57c0a45f69734616e8d9a3718370"},{"id":3.2382779E7,"fileName":"2019052108283143859751.doc","checkContent":"使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行\u003cbr/\u003e采用 C/C++语言编写，可\n以运行在 Linux/Windows/Mac 等操作系统上","returnContent":"e、I/O device、Directory Management、Date/time 类，甚至还包括\n正则表达式的处理功能。 \n4. 支持 2D/3D 图形渲染，支持 OpenGL。 \n4.1.4 OpenCV 介绍 \nOpenCV 是由英特尔公司发起并参与开发的开源计算机视觉库，它以 BSD 许\n可证授权发行，可以在商业和研究领域中免费使用；它采用 C/C++语言编写，可\n以运行在 Linux/Windows/Mac 等操作系统上，同时它还提供了 Python、Ruby、\nMATLAB 以及其它语言的接口[54]；目前 OpenCV 由 500 多个 C 函数和部分 C++\n类构成，实现了图像处理和计算机视觉方面的许多通用算法，这些算法主要应用\n在：人机交互、模式识别、图像分割、运动跟踪、机器人等方面，这为计算机视\n觉领域的系统开发人员提供了方便。总体来说 OpenCV 主体有五个模块，但常用\n的是其中四个主要模块，如图 4-2 所示： \n \n图 4-2 OpenCV 的基本结构 \nCV 模块包含基本的图像处理函数和高级的计算机视觉算法；ML 是机器学习\n \n 44\n库，包含一些基于统计的分类和聚类工具；HighGUI 包含图像和视频输入/输出的\n函数；CXCORE 包含 OpenCV 的一些基本数据结构和相关函数。","keyWordsShow":"\u003cem\u003e使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行\u003c/em\u003e","title":"视频中目标检测与追踪方法的研究与应用","author":"陈波","comeFrom":"博硕","percentage":72.6754,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2011","num":"12","organ":"电子科技大学","textid":"993997761d9136c03624212d4765f9df","category":"CMFD","context":"发起并参与开发的开源计算机视觉库，它以 BSD 许\n可证授权发行，可以在商业和研究领域中免费使用；它\u003cem\u003e采用 C/C++语言编写，可\n以运行在 Linux/Windows/Mac 等操作系统上\u003c/em\u003e，同时它还提供了 Python、Ruby、\nMATLAB 以及其它语言的接口[54]；目前 ","copyCount":1.0,"copyKeywords":"目标检测;目标跟踪;AdaBoost;均值漂移","startIndex":14999.0,"endIndex":15040.0,"checkid":1.33210825E8,"isQueto":0.0,"md5Code":"4e9c57c0a45f69734616e8d9a3718370"},{"id":3.2382679E7,"fileName":"2019052108283143859751.doc","checkContent":"使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行\u003cbr/\u003e采用 C/C++语\n言编写，可以运行在 Linux、Windows 和 Mac 等操作系统","returnContent":"公司开发的一个计算机视觉工具箱，采用 C/C++语\n言编写，可以运行在 Linux、Windows 和 Mac 等操作系统。此外，OpenCV 还提供了\nPython、Ruby、MATLAB 以及其他语言的接口，使用起来非常的方便。目前最新的\n官方版本号是 OpenCV2.4.2。\n2.2.2 OpenCV 的结构模块及内容\nOpenCV 主体分为五大模块，其中四大模块如图 2.2 所示。\n8\n第 2 章 视觉跟踪测控总体研究方案\nCVMLLHighGUI\n图像处理与视觉算法统计分类器GUI，\n图像和视频输入/输出\nCXCORE\n基本结构和算法，XML支持，绘图函数\n图 2.2 OpenCV 的基本结构\n（1）CV 模块：核心函数库，包含了各种基本的图像处理函数和高级的计算机\n视觉算法。\n（2）ML 模块：机器学习库，包含了一些基本统计的分类与聚类工具。\n（3）HighGUI 模块：GUI 函数库，包含了图像以及视频的输入、输出函数。\n（4）CXCORE 模块：数据结构与线性代数库，包含了 OpenCV 的一些基本数据\n结构以及功能定义，同时包含数据处理的相关函数。\n2.","keyWordsShow":"\u003cem\u003e使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行\u003c/em\u003e","title":"移动机器人自主视觉跟踪测控技术研究","author":"龙忠杰","comeFrom":"博硕","percentage":63.7385,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"07","organ":"北京信息科技大学","textid":"2a6acc57319829235964b252b71a5edc","category":"CMFD","context":"公司开发的一个计算机视觉工具箱，\u003cem\u003e采用 C/C++语\n言编写，可以运行在 Linux、Windows 和 Mac 等操作系统\u003c/em\u003e。此外，OpenCV 还提供了\nPython、Ruby、MATLAB 以及其他语言的接口，使","copyCount":1.0,"copyKeywords":"移动机器人;目标跟踪;特征点提取;立体匹配;OpenCV","startIndex":14999.0,"endIndex":15040.0,"checkid":1.33210826E8,"isQueto":0.0,"md5Code":"fb2149734bf2c2f3d2e47adc8743ae60"},{"id":3.2382784E7,"fileName":"2019052108283143859751.doc","checkContent":"语言编写，在Windows、Linux、Mac等操作系统上都可以运行\u003cbr/\u003e语言编写，可以运行在Linux/Windows/Mac等操作系统上","returnContent":"实时火焰视频监控可以直接通过SAA7105输出 端连接。\n由于本系统采用的是双CPU结构,两者之间的通信尤为重要。 因此本系统采用HPI接口来实现S3C2410与DM642之间的通信。HPI 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它包含","copyCount":1.0,"startIndex":15000.0,"endIndex":15040.0,"checkid":1.33210825E8,"isQueto":0.0,"md5Code":"4e9c57c0a45f69734616e8d9a3718370"}],"checkContent":".4 机器学习库\nOpenCV是一个著名的开源计算机视觉函数库，使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行。","endIndex":15041.0,"checkContentShow":"，对比看来，我们选用HOG特征来作为我们图像的描述信息。\n3.3\u003cem\u003e.4 机器学习库\nOpenCV是一个著名的开源计算机视觉函数库，使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行。\u003c/em\u003e其中有两大重要模块，CV模块和ML模块。CV模块主要包含了大量对","chapterNum":0.0,"index":36},"10845":{"num":21.0,"wordNum":56.0,"startIndex":10845.0,"color":"red","copyRate":63.8662,"yyIndexs":[],"list":[{"id":3.2383901E7,"fileName":"2019052108283143859751.doc","checkContent":"核函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\u003cbr/\u003e多项式核函数(PolynomialKernel)是线性不可分SVM常用的核函数之一","returnContent":"2018年11月10日-多项式核函数(PolynomialKernel)是线性不可分SVM常用的核函数之一,公式如下:...libsvm默认的核函数就是它。公式如下:K(x,z)\u003dexp(−γ||x−z||2)...","keyWordsShow":"\u003cem\u003e就是使用的这个核函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\u003c/em\u003e","title":"6.支持向量机(SVM)核函数-hyc339408769-博客园","comeFrom":"互联网","percentage":58.6046,"type":"red","category":"NET","context":"2018年11月10日-\u003cem\u003e多项式核函数(PolynomialKernel)是线性不可分SVM常用的核函数之一\u003c/em\u003e,公式如下:...libsvm默认的核函数就是它。公式如下:K(x,z)\u003dexp(−γ||x","copyCount":4.0,"startIndex":10845.0,"endIndex":10904.0,"checkid":1.33220789E8,"isQueto":0.0,"md5Code":"33bc9c4b5fd1c802ba31a81a42ae2aa7"},{"id":3.2382761E7,"fileName":"2019052108283143859751.doc","checkContent":"函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\u003cbr/\u003e函数 。\n(2)多项式核函数 :多项式核函数是线性不可\n分 SVM常用的核函数之一","returnContent":"若一\n此时将低维空间的特征向量映射至高维空间 ,经\n过映射处理后的特征即有可能线性可分 。因此可\n以构造映射函数来对数据集进行处理 。\n核函数的基本定义如下 :\n设是输入空间 (欧式空间 R的子集或离散\n集合 ),同时 ,设叼为特征空间 (希尔伯特空间 ),\n假设存在一个从到叼的映射咖 ( ):一 7使得对\n所有 , ∈ ,函数 K( ,z)满足条件 K( ,)\u003d咖 ( )·\n(),则认为 K( ,)为核函数 , ( )为映射函\n数 。式中咖 ( )·咖 (z)为 ( )和 (Z)的内积 。\n选择一个适合的核函数对于模型的分类效果\n影响巨大 。常用的核函数有以下几种 :\n(1)线性核函数 :线性核函数即线性可分支\n持向量机 ,表达式为 :K( ,)\u003d ·\n此时可以将线性可分支持向量机与线性不可\n分支持向量机归为一类 ,区别仅仅在于线性可分\n支持向量机用的是线性核函数 。\n(2)多项式核函数 :多项式核函数是线性不可\n分 SVM常用的核函数之一 ,表达式为 :\nK( ,)\u003d(Tx·z+r),其中 , ,r,d都需要白行\n调参定义 。\n(3)高斯核函数 。高斯核函数在 SVM中也称\n为径向基核函数 ,它是应用于非线性分类支持向\n量机算法中最主流的核函数 。","keyWordsShow":"\u003cem\u003e是使用的这个核函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\u003c/em\u003e","title":"基于文本特征的企业微博转发效果影响因素研究","author":"王晓耘 范晶晶 陈思","comeFrom":"期刊","percentage":63.8662,"type":"red","periodical":"生产力研究","periodicalYear":"2018","beginPage":7.0,"endPage":7.0,"num":"5","organ":"杭州电子科技大学管理学院","textid":"621de829e90995074580e9199ceb227e","category":"CJFD","context":"将线性可分支持向量机与线性不可\n分支持向量机归为一类 ,区别仅仅在于线性可分\n支持向量机用的是线性核\u003cem\u003e函数 。\n(2)多项式核函数 :多项式核函数是线性不可\n分 SVM常用的核函数之一\u003c/em\u003e ,表达式为 :\nK( ,)\u003d(Tx·z+r),其中 , ,r,d都需要白行\n调参定义 。\n","copyCount":4.0,"startIndex":10846.0,"endIndex":10904.0,"checkid":1.33210751E8,"isQueto":0.0,"md5Code":"82ed6a2495ce03d3f5338e4cc75753a2"},{"id":3.2384582E7,"fileName":"2019052108283143859751.doc","checkContent":"函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\u003cbr/\u003e多项式核函数(PolynomialKernel)是线性不可分SVM常用的核函数之一","returnContent":"2019年4月1日-_2)\\),通过这个改进的五元样本特征,我们重新把不是线性回归的函数变回线性...多项式核函数(PolynomialKernel)是线性不可分SVM常用的核函数之一,表达...","keyWordsShow":"\u003cem\u003e是使用的这个核函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\u003c/em\u003e","title":"支持向量机原理(三)线性不可分支持向量机与核函数-刘建..._博客园","comeFrom":"互联网","percentage":51.7364,"type":"red","category":"NET","context":"019年4月1日-_2)\\),通过这个改进的五元样本特征,我们重新把不是线性回归的函数变回线性...\u003cem\u003e多项式核函数(PolynomialKernel)是线性不可分SVM常用的核函数之一\u003c/em\u003e,表达...","copyCount":4.0,"startIndex":10846.0,"endIndex":10904.0,"checkid":1.3322079E8,"isQueto":0.0,"md5Code":"8549555f2c4ab6964d5a0dadb7abf35f"},{"id":3.238295E7,"fileName":"2019052108283143859751.doc","checkContent":"多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数\u003cbr/\u003eSVM中比较常用三种核函数⑷]:1)线性核(linearkernel): K{x-y)-{x\u0027y)2)多项式核","returnContent":"8)上面介绍的是SVM处理线性情况的方法,在处理非线性的问题的时候,就需要对上的线性SVM进行一个扩展了。需要借助非线性映射,把问题转化为高维空间中上,在新的高维空间上再用线性SVM[方法求得最佳超平面。从公示(5.6)、(5.7)和(5.8)可以看出,求解的最优分类函数只是和支持向量的内积(x,?功有关系。在高维空间上只需要在高维空间上对它的内积进行运算,是可以借助原空间的核函数实现这些运算。因此,在求解非线性分类问题的时候,只要找到合适的尤就可以把低维空间映射高维空间了,进而可以实现非线性问题变换后的线性分类,重要的是,这样做并不会使算法复杂度有所增加。这个时候的目标函数就变为:n 1 nQ{a)\u003d2]\u003c2/--2]aiOjyiyjKixi■xj) (5.9)i\u003dl 2戶/最终的分类函数变为:f(X)\u003dsgn{(w**x)+b)\u003dsgn{[“/*yiK{xi?x)+b*)(5.10)使用不同的核函数会构造出不同的SVM算法,下面列举的是在SVM中比较常用三种核函数⑷]:1)线性核(linearkernel): K{x-y)-{x\u0027y)2)多项式核(polynomialkernel): ?少)\u003d[?y(x,3/)+c广其中s、c、为参数,线性核函数可以看成是多项式核函数的特殊情况。","keyWordsShow":"\u003cem\u003e多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\n（\u003c/em\u003e","title":"睡眠脑电自动分期方法研究","author":"郭超珍","comeFrom":"博硕","percentage":50.4617,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2015","num":"10","organ":"广东工业大学","textid":"b9cf6e286509d22b5f3d36ee26bf7c7e","category":"CMFD","context":"“/*yiK{xi?x)+b*)(5.10)使用不同的核函数会构造出不同的SVM算法,下面列举的是在\u003cem\u003eSVM中比较常用三种核函数⑷]:1)线性核(linearkernel): K{x-y)-{x\u0027y)2)多项式核\u003c/em\u003e(polynomialkernel): ?少)\u003d[?y(x,3/)+c广其中s、c、为参数,","copyCount":3.0,"copyKeywords":"睡眠分期;脑电信号(EEG);样本熵;希尔伯特黄变换(HHT);支持向量机(SVM)","startIndex":10859.0,"endIndex":10905.0,"checkid":1.3321075E8,"isQueto":0.0,"md5Code":"316029cbe47faea6a447df9bda24f945"},{"id":3.2382762E7,"fileName":"2019052108283143859751.doc","checkContent":"多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数\u003cbr/\u003e多项式核函数（Polynomial Kernel Function)在SVM中并不常用","returnContent":"最终SVM模型的输入特征向量由最近时间节点的有功功率数据，最近时间节点\n的气象数据，以及相似日的有功功率历史数据共同组成。\n21\n 北京邮电大学下程硕士学位论文\n4.3 SVM模型核函数的选择\n正如背景理论及相关技术章节介绍，SVM的核函数需要满足Mercer条件。SVM\n模型由训练样本集和核函数决定，选取不同的核函数，会生成不同的回归模型。SVM\n一般常用的核函数类型有：线性核函数，多项式核函数，径向基核函数和Sigmoid核\n函数。四种常用的核函数他们各具有一定的特点：\n线性核函数（Linear Kernel Function)主要用于线性可分的情形，无法将数据映射\n到高维空间。参数少，速度快，对于一般数据，分类效果可以达到较为理想的效果。\n多项式核函数（Polynomial Kernel Function)在SVM中并不常用，多用于NLP自\n然语言处理的场景中。\n径向基核函数（Radical Basis Kernel Function)又称为高斯核函数，主要用于线性\n不可分的情形。参数多，参数对分类结果影响较大。在没有先验知识的情况下，采用\n径向基核函数的表现往往较好，因为其空间复杂度较小，易于实现，比起线性核函数\n和多项式核函数，首先径向基核函数可以将数据映射至高维，这是线性核函数无法做\n到的，而参数调整优化方面，径向基核函数需要调整的参数个数较少。","keyWordsShow":"\u003cem\u003e多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\n（\u003c/em\u003e","title":"基于SVM的输变电设备有功功率短期预测的设计与实现","author":"唐晔","comeFrom":"博硕","percentage":60.3079,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"04","organ":"北京邮电大学","textid":"d72a56c775e4a0d3250519dbf1fadee0","category":"CMFD","context":"的情形，无法将数据映射\n到高维空间。参数少，速度快，对于一般数据，分类效果可以达到较为理想的效果。\n\u003cem\u003e多项式核函数（Polynomial Kernel Function)在SVM中并不常用\u003c/em\u003e，多用于NLP自\n然语言处理的场景中。\n径向基核函数（Radical Basis Kerne","copyCount":3.0,"copyKeywords":"SVM;回归预测;参数优化;Spark;并行化","startIndex":10859.0,"endIndex":10904.0,"checkid":1.33210751E8,"isQueto":0.0,"md5Code":"82ed6a2495ce03d3f5338e4cc75753a2"}],"checkContent":"就是使用的这个核函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\n","endIndex":10905.0,"checkContentShow":"名思义，这是一种用于解决线性可分问题的核函数，而我们在课题研究中\u003cem\u003e就是使用的这个核函数\n（2）多项式核函数（Polynomial Kernel）\n这是线性不可分SVM中常用的核函数之一\n\u003c/em\u003e（3）高斯核函数（Gaussian Kernel）\n在SVM中也","chapterNum":0.0,"index":21},"17166":{"num":39.0,"wordNum":46.0,"startIndex":17166.0,"color":"red","copyRate":51.5998,"yyIndexs":[],"list":[{"id":3.2382707E7,"fileName":"2019052108283143859751.doc","checkContent":"也不能过大。\n3.5 检测图像\n3.5.1 检测窗口的大小\n在图像进行检测的时候检测窗口\u003cbr/\u003e为避免窗口过大所出现的问题，可以采用事先检测图像边界的方法来定义\n窗口的大小","returnContent":"7)\n 当c伽，妙)达到最大时，得到左图像中的点(xL，y‘)与右图像中的点\n(x:+公，，:+匀)相匹配‘，4，。\n(2)窗口大小的选择\n 与基于特征的匹配算法不同，该方法是以窗口为单位进行匹配计算的，所选择的窗\n口要求含有足够的亮度变化，因此选择适当的窗口的大小成为生成平滑且准确的视差图\n大连理工大学硕士学位论文\n的关键。窗口选择的过小时，由于不能包含足够的亮度变化，使得亮度变化与图像噪声\n的比率很小，从而只能得到一个含有很大误差的视察估计值;同样，若窗口定义的过大，\n当所定义的窗口区域中点的深度值发生突变时，采用上述计算准则得到的值将不能表示\n正确的匹配，此时，窗口定义的越大，匹配效果越差，而且窗口越大运算量越大，运算\n的时间越长。为避免窗口过大所出现的问题，可以采用事先检测图像边界的方法来定义\n窗口的大小，但边界的检测又是一个很棘手的问题。现有的基于区域的立体匹配算法中\n很多都是使用固定的窗口大小来进行立体匹配计算的[35一〕。\n 基于区域的匹配算法的实质是利用了局部窗口之间灰度信息的相关程度，这种方法\n在地势平坦而纹理丰富的地方可以达到比较高的精度，并且能取得致密的视差场。","keyWordsShow":"\u003cem\u003e样本集中的图片也不能过大。\n3.5 检测图像\n3.5.1 检测窗口的大小\n在图像进行检测的时候检测窗口\u003c/em\u003e","title":"基于区域增长的立体匹配算法的研究","author":"孟晶晶","comeFrom":"博硕","percentage":51.5998,"type":"red","periodical":"中国优秀博硕士学位论文全文数据库 (硕士)","periodicalYear":"2006","num":"02","organ":"大连理工大学","textid":"ad742f3a64de3894d72c91e892eddded","category":"CMFD","context":"不能表示\n正确的匹配，此时，窗口定义的越大，匹配效果越差，而且窗口越大运算量越大，运算\n的时间越长。\u003cem\u003e为避免窗口过大所出现的问题，可以采用事先检测图像边界的方法来定义\n窗口的大小\u003c/em\u003e，但边界的检测又是一个很棘手的问题。现有的基于区域的立体匹配算法中\n很多都是使用固定的窗口大","copyCount":9.0,"copyKeywords":"双目立体匹配;Harris角点;区域增长;视差图","startIndex":17166.0,"endIndex":17217.0,"checkid":1.33210859E8,"isQueto":0.0,"md5Code":"6585e439f5fbef3eba8dba508c343b4a"}],"checkContent":"样本集中的图片也不能过大。\n3.5 检测图像\n3.5.1 检测窗口的大小\n在图像进行检测的时候检测窗口","endIndex":17217.0,"checkContentShow":"，这样我们放弃了提取特征时长换来的更多信息也得不偿失了，因此我们\u003cem\u003e样本集中的图片也不能过大。\n3.5 检测图像\n3.5.1 检测窗口的大小\n在图像进行检测的时候检测窗口\u003c/em\u003e的大小也是我们需要控制的因素，这也是会直接影响我们最终的结果。如","chapterNum":0.0,"index":39},"8427":{"num":15.0,"wordNum":74.0,"startIndex":8427.0,"color":"red","copyRate":63.7128,"yyIndexs":[{"startIndex":8500.0,"endIndex":8504.0}],"list":[{"id":3.2382884E7,"fileName":"2019052108283143859751.doc","checkContent":"识别，通常与AdaBoost算法进行结合使用。\n2.2.2 HOG特征提取原理\nHOG特征的提取方式是通过计算图像的梯度方向直方图。梯度方向\u003cbr/\u003e与目标识别算法中。\n2.2.2 HOG 特征提取算法的实现流程\nHOG 特征是通过计算和统计图像局部区域内的梯度方向直方图","returnContent":"在\nHOG 提取过程中将图像梯度方向划分为很多小细胞元，然后在这些小细胞元的基础上进\n行操作，对图像大范围的空间变换不敏感。其次是 HOG 特征对目标的姿态变化具有很强\n的鲁棒性，这是因为在 HOG 特征提取过程中，经过了粗的空域抽样以及局部光学归一化\n的操作，能够容忍目标的部分形变。由于 HOG 局部特征的这些优点，它才被广泛应用于\n目标检测，目标跟踪与目标识别算法中。\n2.2.2 HOG 特征提取算法的实现流程\nHOG 特征是通过计算和统计图像局部区域内的梯度方向直方图来构成特征的，因为在基于局部特征提取的目标检测与跟踪技术研究\n10\n图像中局部目标图像的外观和形状能够被局部区域内的梯度方向和边缘方向的统计直方\n图细致的表现出来，即使在没有任何有关梯度和边缘信息的条件下，完整的 HOG 特征算\n法流程如图(2.1)所示。\n（1）第一步对整幅图像进行标准化，其目的是为了减小光照的影响。在实际的计算过\n程中首先对图像进行压缩，由于图像局部的表层曝光对图像表面的纹理特征影响较大，通\n过在 gamma 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特征是通过计算和统计图像局部区域内的梯度方向直方图\u003c/em\u003e来构成特征的，因为在基于局部特征提取的目标检测与跟踪技术研究\n10\n图像中局部目标图像的外观","copyCount":6.0,"copyKeywords":"局部特征提取;支持向量机;目标检测;空时上下文;目标跟踪","startIndex":8427.0,"endIndex":8504.0,"checkid":1.33210714E8,"isQueto":0.0,"md5Code":"97436742284863b0dd93f0900fa788ee"},{"id":3.2382882E7,"fileName":"2019052108283143859751.doc","checkContent":"HOG特征提取原理\nHOG特征的提取方式是通过计算图像的梯度方向直方图。梯度方向\u003cbr/\u003eHOG 特\n征的提取。这种方法是通过对待检测图像进行密集扫描的窗口计算局部梯度方向","returnContent":"有很多种分类器可以用于这个目的，一般使用 SVM 和 AdaBoost 较多，\n第三章已经介绍过这些分类器的原理。在本章的人体检测系统中，与 Dalal 的方\n法一样，使用线性 SVM 作为我们的二值分类器，因为这种分类器在我们的样本\n库上具有非常准确可靠的效果。 \n在检测阶段中，输入的待检测图像在各个缩放级别和位置被扫描。对于每一\n个缩放级别和位置，计算检测窗口的特征向量，和训练过程一样，然后使用二值\n分类器进行人/非人的判断。通常如果一个区域有人体目标会检测出相互交错的\n多个窗口，最后我们要将同一个目标的多个窗口融合成一个包围窗口，称为包围\n盒。检测结果很大程度上也取决于待检测图像扫描的密度和最后融合包围盒算法\n的好坏。 \n \n4.3 HOG 特征提取 \n本节将讨论 HOG 特征的提取过程，在训练和检测过程中都要进行 HOG 特\n征的提取。这种方法是通过对待检测图像进行密集扫描的窗口计算局部梯度方向\n 36\n第四章 基于 HOG 特征的快速人体检测算法 \n取得的。因为局部目标的外观形状通常可以由梯度方向和强度来表示。图 4-2 给\n出了完整的 HOG 特征提取算法和过程。 \n \n检测窗口 \n 归一化图像 \n 计算梯度 \n对于每一个 cell 块对梯\n度直方图进行规定权\n重的投影 \n对于每一个重叠 block\n块内的 cell 进行对比度\n归一化 \n把所有block内的直方\n图向量一起组合成一\n个大的HOG特征向量 \nCell \nBlock \n特征向量f\u003d{x1,x2,…….","keyWordsShow":"\u003cem\u003e.2.2 HOG特征提取原理\nHOG特征的提取方式是通过计算图像的梯度方向直方图。梯度方向\u003c/em\u003e","title":"基于梯度方向直方图的快速人体检测算法","author":"周健","comeFrom":"博硕","percentage":63.7128,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2009","num":"S2","organ":"天津大学","textid":"9ab783018481af103e4c4f42df8aedec","category":"CMFD","context":" \n \n4.3 HOG 特征提取 \n本节将讨论 HOG 特征的提取过程，在训练和检测过程中都要进行 \u003cem\u003eHOG 特\n征的提取。这种方法是通过对待检测图像进行密集扫描的窗口计算局部梯度方向\u003c/em\u003e\n 36\n第四章 基于 HOG 特征的快速人体检测算法 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HOG特征提取Dalal[7]于2005年提出了梯度方向直方图特征,即HOG特征,这是针对图像某个矩形区域中的梯度方向与强度的统计信息而定义的一种特征.HOG特征提取的具体步骤如下:(1)采用Gamma校正法对输入图像进行颜色空间标准化,目的是调整图像的对比度,抑制噪音的影响.(2)使用[-1,0,1]模板计算样本图像的梯度,求出每个像素点梯度的模值和方向[3].(3)采用高斯滤波法对每个block区域中的图像梯度进行高斯加权.(4)选用三线性插值[8]法加权计算梯度方向直方图向量.","keyWordsShow":"\u003cem\u003eHOG特征的提取方式是通过计算图像的梯度方向直方图。梯度方向\u003c/em\u003e","title":"基于线性SVM的车辆前方行人检测方法","author":"溪海燕 肖志涛 张芳","comeFrom":"期刊","percentage":57.8832,"type":"red","periodical":"天津工业大学学报","periodicalYear":"2012","vol":"v.31;No.142","beginPage":71.0,"endPage":75.0,"num":"01","organ":"天津工业大学学报","textid":"aafee5da723c3520921bc38aab6607e9","category":"CJFD","context":"窗口融合[6]获得检测结果.利用本文方法处理行人视频,可实现对复杂交通背景中运动遮挡行人的检测.1 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design and implementation of the image detection\u003cbr/\u003eof dynamic human body image, and design the edge detection","returnContent":"ainstream of edge detection\noperator and FPGA processing chips. For the purpose of implementation of real time edge\ndetection of dynamic human body image, and design the edge detection system for human\ngait video images which is based on Sobel operator.\n★In this thesis, image-processing system is designed for two parts, external functional\nhardware and internal logic function device. External hardware mainly includes the camera\nand LCD screen that is used for real-time display. 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主要针对线性可分情况进行分析，对于线性不可分的情况，利用升维\n将其线性化。升维，就是将样本映射到高维空间，这种情况一般会增加计算的复\n杂性，甚至引起“维数灾难”。\n10\nSVM 应用核函数的展开定理，很好地解决了升维导致计算复杂化的难题。\n相比于线性模型，SVM 在高维特征空间建立线性学习机，基本上不增加计算的\n复杂性，且在某种程度上避免了“维数灾难”。SVM 中常用的核函数包括以下 4\n种：\n（1）线性核函数： K ( x,y)\u003dx y；\n（2）多项式核函数： K ( x,y)\u003d [(x y)+1]d；\n（3）径向基函数： K (x,y)\u003d exp( (x y)2/d2)；\n（4）二层神经网络核函数： K ( x,y)\u003d tanh[a(x y)+b]。","keyWordsShow":"\u003cem\u003e\u003cspan class\u003d\"yellow\"\u003e的核函数而且是SVM中最常用的线性不可分的核函数[7]\u003c/span\u003e。\n（4）Sigmod核函数\u003c/em\u003e","title":"跨平台赤潮藻显微图像分析库设计与实现","author":"石珍生","comeFrom":"博硕","percentage":55.1401,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2012","num":"02","organ":"中国海洋大学","textid":"c08ded6e4826cbf8c57bb9ddafda0272","category":"CMFD","context":"SVM 在高维特征空间建立线性学习机，基本上不增加计算的\n复杂性，且在某种程度上避免了“维数灾难”。\u003cem\u003eSVM 中常用的核函数包括以下 4\n种：\n（1）线性核函数\u003c/em\u003e： K ( x,y)\u003dx y；\n（2）多项式核函数： K ( x,y)\u003d [(x y)+1]","copyCount":2.0,"copyKeywords":"赤潮藻;跨平台;图像处理;模式识别;OpenCV;libsvm;Android;ThinkPHP","startIndex":10985.0,"endIndex":11026.0,"checkid":1.33210752E8,"isQueto":0.0,"md5Code":"039fdf9cda58c9b028c12f304a1a4ca1"},{"id":3.2382879E7,"fileName":"2019052108283143859751.doc","checkContent":"核函数[7]。\n（4）Sigmod核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数\u003cbr/\u003e核函数：\nK(7)u,v(8) (28)tanh(7)a(7)u ?v(8) (10)b(8) （2-14）\n式（2-14）即为 sigmod 核函数","returnContent":"8)dK u v (28)u ?v (10) （2-12）\n式（2-12）为 d 阶的多项式核函数，其中 d 为多项式的阶数。\n（2）高斯径向基核函数（RBF）：\n(7) (8) (7) (8)22K u, v (28)exp -u -v/ 2? （2-13）\n式（2-13）中，核参数? 为高斯函数的宽度，RBF 核具有优异的泛化性能，\n因此是目前使用最广泛的一种核函数。\n这个核函数能够将原始样本空间映射到无穷维空间，高维项的权重衰减速度\n与? 的取值大小有关。? 的值越大，衰减的越快，映射的维数越低。? 取值越小，\nSVM 的分类能力就越好，即 VC 维越大，但是这会带来过拟合问题。\n（3）sigmod 核函数：\nK(7)u,v(8) (28)tanh(7)a(7)u ?v(8) (10)b(8) （2-14）\n式（2-14）即为 sigmod 核函数，其中 a 是 sigmod 核函数的参数，但是其只有\n在特定的 a 和b 的情况下才满足 Mercer 条件。\n除此之外，还有小波核函数、傅里叶核函数等其他类型的核函数。 支持向量机算法的入侵检测分类研究\n 14\n2.2.4 支持向量机参数的评价\n在上节中已经介绍了支持向量机这种分类方法。","keyWordsShow":"\u003cem\u003e\u003cspan class\u003d\"yellow\"\u003e的线性不可分的核函数[7]\u003c/span\u003e。\n（4）Sigmod核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数之一。\u003c/em\u003e","title":"支持向量机算法的入侵检测分类研究","author":"张博伦","comeFrom":"博硕","percentage":48.005,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2018","num":"10","organ":"青岛科技大学","textid":"a03e0a5242aecddb631b3bb0f565c27d","category":"CMFD","context":"值越小，\nSVM 的分类能力就越好，即 VC 维越大，但是这会带来过拟合问题。\n（3）sigmod \u003cem\u003e核函数：\nK(7)u,v(8) (28)tanh(7)a(7)u ?v(8) (10)b(8) （2-14）\n式（2-14）即为 sigmod 核函数\u003c/em\u003e，其中 a 是 sigmod 核函数的参数，但是其只有\n在特定的 a 和b 的情况下才满足 ","copyCount":4.0,"copyKeywords":"支持向量机;入侵检测;多元宇宙优化算法;混沌映射","startIndex":10999.0,"endIndex":11065.0,"checkid":1.33210753E8,"isQueto":0.0,"md5Code":"2269261e1e4e76131af1d8efc7693b9e"},{"id":3.238288E7,"fileName":"2019052108283143859751.doc","checkContent":"核函数[7]。\n（4）Sigmod核函数（Sigmod Kernel）\n该核函数也是线性\u003cbr/\u003e核函数的识别精度为 75.7%；SIGMOD 核函数\n的识别精度，为 68.7%；线性","returnContent":"在 50 个采样人体的采样数据集上，\n本文测量的识别率结果如图 6-4 所示。 \n \n图 6-3 不同核函数的基于 SVM 识别方法的识别准确率对比图 \n在图 6-3 中，采用了四种颜色来代表不同的核函数。红色代表线性核函数，绿\n色代表多项式核函数，蓝色代表径向基核函数，粉色代表 SIGMOD 核函数。对应\n颜色的虚线也表示了这四种核函数在不同测试样本数量下的平均识别率。 \n从图 6-3 的结果中可以发现，在这四种组最常使用的核函数中，多项式核函数\n的识别精度最高，为 87%；径向基核函数的识别精度为 75.7%；SIGMOD 核函数\n的识别精度，为 68.7%；线性核函数为 60.4%。所以本文优先选取识别精度最高的\n识别方法，即基于多项式核函数的 SVM 支持向量机识别方法。 \n6.2.3 三种识别方法的识别准确率 \n由于考虑到识别过程的计算量、计算复杂程度和能耗，本文设计了三种识别\n方法：基于曲线相似度 CC 的识别算法、基于曲线距离差 CDD 的识别算法以及基\n于 SVM 支持向量机的识别算法。 这三种识别方法均在第五章中给出了详细的步骤\n介绍。","keyWordsShow":"\u003cem\u003e\u003cspan class\u003d\"yellow\"\u003e的线性不可分的核函数[7]\u003c/span\u003e。\n（4）Sigmod核函数（Sigmod Kernel）\n该核函数也是线性\u003c/em\u003e","title":"移动设备上基于电磁吸收率的人体识别 系统研究与设计","author":"杨文玉","comeFrom":"博硕","percentage":54.5614,"type":"red","periodicalYear":"2016","beginPage":88.0,"endPage":88.0,"organ":"电子科技大学","textid":"67c9ac09bad305205f2152ce1a6295e4","category":"CMFD","context":"3 的结果中可以发现，在这四种组最常使用的核函数中，多项式核函数\n的识别精度最高，为 87%；径向基\u003cem\u003e核函数的识别精度为 75.7%；SIGMOD 核函数\n的识别精度，为 68.7%；线性\u003c/em\u003e核函数为 60.4%。所以本文优先选取识别精度最高的\n识别方法，即基于多项式核函数的 SVM","copyCount":4.0,"startIndex":10999.0,"endIndex":11050.0,"checkid":1.33210753E8,"isQueto":0.0,"md5Code":"2269261e1e4e76131af1d8efc7693b9e"},{"id":3.2383794E7,"fileName":"2019052108283143859751.doc","checkContent":"核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见\u003cbr/\u003e核函数SVM之解决线性不可分写在SVM之前——凸优化...解决方法之一就是","returnContent":"2015年3月23日-SVM之对偶问题\u003e\u003e\u003eSVM之核函数SVM之解决线性不可分写在SVM之前——凸优化...解决方法之一就是将数据,或者更加正式的称为特征,向高维映射,以期待映射...","keyWordsShow":"\u003cem\u003eigmod核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数之一。\u003c/em\u003e","title":"SVM之核函数-Tswaf-博客园","comeFrom":"互联网","percentage":57.9787,"type":"red","category":"NET","context":"2015年3月23日-SVM之对偶问题\u0026gt;\u0026gt;\u0026gt;SVM之\u003cem\u003e核函数SVM之解决线性不可分写在SVM之前——凸优化...解决方法之一就是\u003c/em\u003e将数据,或者更加正式的称为特征,向高维映射,以期待映射...","copyCount":3.0,"startIndex":11018.0,"endIndex":11065.0,"checkid":1.33220793E8,"isQueto":0.0,"md5Code":"5af0d78bd0d6e66aeb200f28a1db4374"},{"id":3.2382988E7,"fileName":"2019052108283143859751.doc","checkContent":"核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数之一\u003cbr/\u003e核函数 :多项式核函数是线性不可\n分 SVM常用的核函数之一","returnContent":"若一\n此时将低维空间的特征向量映射至高维空间 ,经\n过映射处理后的特征即有可能线性可分 。因此可\n以构造映射函数来对数据集进行处理 。\n核函数的基本定义如下 :\n设是输入空间 (欧式空间 R的子集或离散\n集合 ),同时 ,设叼为特征空间 (希尔伯特空间 ),\n假设存在一个从到叼的映射咖 ( ):一 7使得对\n所有 , ∈ ,函数 K( ,z)满足条件 K( ,)\u003d咖 ( )·\n(),则认为 K( ,)为核函数 , ( )为映射函\n数 。式中咖 ( )·咖 (z)为 ( )和 (Z)的内积 。\n选择一个适合的核函数对于模型的分类效果\n影响巨大 。常用的核函数有以下几种 :\n(1)线性核函数 :线性核函数即线性可分支\n持向量机 ,表达式为 :K( ,)\u003d ·\n此时可以将线性可分支持向量机与线性不可\n分支持向量机归为一类 ,区别仅仅在于线性可分\n支持向量机用的是线性核函数 。\n(2)多项式核函数 :多项式核函数是线性不可\n分 SVM常用的核函数之一 ,表达式为 :\nK( ,)\u003d(Tx·z+r),其中 , ,r,d都需要白行\n调参定义 。\n(3)高斯核函数 。高斯核函数在 SVM中也称\n为径向基核函数 ,它是应用于非线性分类支持向\n量机算法中最主流的核函数 。","keyWordsShow":"\u003cem\u003eigmod核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数之一\u003c/em\u003e","title":"基于文本特征的企业微博转发效果影响因素研究","author":"王晓耘 范晶晶 陈思","comeFrom":"期刊","percentage":53.3447,"type":"red","periodical":"生产力研究","periodicalYear":"2018","beginPage":7.0,"endPage":7.0,"num":"5","organ":"杭州电子科技大学管理学院","textid":"621de829e90995074580e9199ceb227e","category":"CJFD","context":"线性不可\n分支持向量机归为一类 ,区别仅仅在于线性可分\n支持向量机用的是线性核函数 。\n(2)多项式\u003cem\u003e核函数 :多项式核函数是线性不可\n分 SVM常用的核函数之一\u003c/em\u003e ,表达式为 :\nK( ,)\u003d(Tx·z+r),其中 , ,r,d都需要白行\n调参定义 。\n","copyCount":3.0,"startIndex":11018.0,"endIndex":11064.0,"checkid":1.33210754E8,"isQueto":0.0,"md5Code":"1f1a1721c2620ea0e83562a77cbf0648"}],"checkContent":"在SVM中也被称为径向基函数(Radial Basis Function, RBF)是libsvm中默认使用的核函数而且是SVM中最常用的线性不可分的核函数[7]。\n（4）Sigmod核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数之一。","endIndex":11065.0,"checkContentShow":"核函数之一\n（3）高斯核函数（Gaussian Kernel）\n\u003cem\u003e在SVM中也被称为径向基函数(Radial Basis Function, RBF)是libsvm中默认使用的核函数而且是SVM中最常用的线性不可分的核函数[7]。\n（4）Sigmod核函数（Sigmod Kernel）\n该核函数也是线性不可分SVM常见的核函数之一。\u003c/em\u003e\n2.4 NMS算法\n2.4.1 NMS算法介绍\nNMS即是非极","chapterNum":0.0,"index":22},"13052":{"num":28.0,"wordNum":50.0,"startIndex":13052.0,"color":"red","copyRate":52.1234,"yyIndexs":[],"list":[{"id":3.2382881E7,"fileName":"2019052108283143859751.doc","checkContent":"检测，提取出检测窗口的HOG特征，使用人头分类器进行预测，得到是人头的检测窗口\u003cbr/\u003e检测窗口，提取待检测窗口的 HOG 特征，送入训练好的 SVM\n分类器得到检测","returnContent":"此后的很多行人检测算法都是在 HOG-SVM 算法的基础上进\n行深入和改进的。\nHOG 提取的是图像的梯度特征，可以很好的描述目标的形状信息，可用于检\n测具有良好形状特征的目标，梯度特征对几何形变和光学形变都有良好的不变性，\n同时目标本身的一些略微形变并不会影响到检测结果，是一种良好的可用于目标\n检测的特征。\nHOG 特征提取的大致思路是：将待检测窗口分成若干个连通的小区域，称之\n为 Cell（细胞单元）；计算 Cell 内每个点的梯度特征；根据梯度的方向将 Cell 内\n每个点的梯度的模投影到特征向量对应的维度；将若干个 Cell 合并为一个较大的\n区域，称之为 Block（块）；在块内对特征向量进行归一化运算；将所有 Block 的\n特征向量合并，得到待检测窗口的特征向量。再使用SVM分类器进行训练和检测。\n3.1.2 检测流程\nHOG-SVM 目标检测算法分为训练和检测 2 个部分，首先从特定的训练图像\n样本中提取 HOG 特征，送入 SVM 分类器进行训练，得到可用于检测的 SVM 分\n类器。\n通过训练样本训练分类器之后，就可以用于目标实际检测，由于 HOG 特征提\n取处理的图像大小是固定的，因此对于一副实际图像的检测，需要用多尺度模型和\n滑窗的方式得到待检测窗口，提取待检测窗口的 HOG 特征，送入训练好的 SVM\n分类器得到检测结果。","keyWordsShow":"\u003cem\u003e滑动窗口来进行检测，提取出检测窗口的HOG特征，使用人头分类器进行预测，得到是人头的检测窗口的坐标，\u003c/em\u003e","title":"基于CUDA加速的目标检测算法研究","author":"王润强","comeFrom":"博硕","percentage":52.1234,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2018","num":"09","organ":"电子科技大学","textid":"71d6c418e57aa8ab934528b200593eea","category":"CMFD","context":" 特征提\n取处理的图像大小是固定的，因此对于一副实际图像的检测，需要用多尺度模型和\n滑窗的方式得到待\u003cem\u003e检测窗口，提取待检测窗口的 HOG 特征，送入训练好的 SVM\n分类器得到检测\u003c/em\u003e结果。","copyCount":1.0,"copyKeywords":"GPU;CUDA;HOG;卷积神经网络;实时性","startIndex":13052.0,"endIndex":13102.0,"checkid":1.33210789E8,"isQueto":0.0,"md5Code":"91e5a9fff06494a506a0f945fcb7bb53"}],"checkContent":"滑动窗口来进行检测，提取出检测窗口的HOG特征，使用人头分类器进行预测，得到是人头的检测窗口的坐标，","endIndex":13102.0,"checkContentShow":"到一个能预测出是否是人头的分类器。当有图片需要检测时，使用固定的\u003cem\u003e滑动窗口来进行检测，提取出检测窗口的HOG特征，使用人头分类器进行预测，得到是人头的检测窗口的坐标，\u003c/em\u003e然后使用NMS算法将一些重合窗口进行进行去重操作，得到优化后的检","chapterNum":0.0,"index":28},"9332":{"num":18.0,"wordNum":57.0,"startIndex":9332.0,"color":"red","copyRate":84.0217,"yyIndexs":[{"startIndex":9332.0,"endIndex":9381.0}],"list":[{"id":3.2382831E7,"fileName":"2019052108283143859751.doc","checkContent":"只是行人检测，由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]，因此在其他的检测\u003cbr/\u003e行人检测问题，但由于它对光照变化、\n阴影等噪声具有较好的鲁棒性，并且能够产生良好的检测效果，因此","returnContent":"需要重新统计矩形中黑白区域的像素和，因而在很大程度上降低了特征值计算\n的效率和检测速度。再者，由于 Haar 特征之间互不相关，因此单独使用 Haar\n特征并不能很好的区别出行人与非行人，所以 Haar 特征并不适合用于行人检测。\n但随着 Haar 特征相关研究的深入，后来的许多研究学者也会根据需要将原始的\nHaar 特征进行不同的扩展，进而达到不同的检测目的。\n2.HOG 特征\n梯度方向直方图(Histogram of Oriented Gradient,HOG)是一种用于物体检测\n的特征描述子，它能够很好地运用梯度或边缘方向的密度分布来描述一个目标\n的外观或形状[9-11]。虽然 HOG 特征与其他特征如 SIFT 特征有相似之处，但 HOG\n特征在大小一样的密集网格中进行计算的方式有别于其他特征的计算方法，即华侨大学硕士学位论文\n10\nHOG 特征能够通过对部分重叠区域进行计算及归一化处理来提高其精确度。最\n开始，HOG 特征只被用于研究静态图像的行人检测问题，但由于它对光照变化、\n阴影等噪声具有较好的鲁棒性，并且能够产生良好的检测效果，因此，HOG 特\n征很快就获得了众多研究者的关注，并被应用到了静态图像的车辆检测和视频\n图像的行人检测当中，成为了目前使用较为广泛的一种图像特征。","keyWordsShow":"\u003cem\u003e\u003cspan class\u003d\"yellow\"\u003e然不只是行人检测，由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]\u003c/span\u003e，因此在其他的检测\u003c/em\u003e","title":"人物图像处理技术研究及其在Web端实现","author":"陈丽枫","comeFrom":"博硕","percentage":61.8291,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"12","organ":"华侨大学","textid":"238f6c5770d7d9f004ab45139d181368","category":"CMFD","context":"够通过对部分重叠区域进行计算及归一化处理来提高其精确度。最\n开始，HOG 特征只被用于研究静态图像的\u003cem\u003e行人检测问题，但由于它对光照变化、\n阴影等噪声具有较好的鲁棒性，并且能够产生良好的检测效果，因此\u003c/em\u003e，HOG 特\n征很快就获得了众多研究者的关注，并被应用到了静态图像的车辆检测和视频\n图像的行","copyCount":2.0,"copyKeywords":"图像处理技术;HTML5;人脸检测与跟踪;行人检测","startIndex":9332.0,"endIndex":9390.0,"checkid":1.33210727E8,"isQueto":0.0,"md5Code":"e34962e5631cdb6d2e264834e84e854f"},{"id":3.238283E7,"fileName":"2019052108283143859751.doc","checkContent":"由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]，因此在其他的检测\u003cbr/\u003e由于它对光照变化、阴影等噪声具有较好的鲁棒性，并且能够产生良好的检测效果，因此","returnContent":"将待检测窗口中所有Ｂｌｏｃｋ中的梯度方向直方图组成特征向量，并通过分类器判断当前所检测的对象是目标，还是非目标．虽然ＨＯＧ特征与其他的特征有相似之处，如ＳＩＦＴ特征，但ＨＯＧ特征能够通过对部分重叠区域进行计算及归一化处理，从而提高精确度［９?１１］．由于它对光照变化、阴影等噪声具有较好的鲁棒性，并且能够产生良好的检测效果，因此，ＨＯＧ特征成为目前使用较为广泛的一种图像特征．３犅犘神经网络ＢＰ神经网络是一种按照误差逆向传播算法训练的多层前馈网络［１２?１３］，包括学习期和工作期两个阶段．当处于学习期时，ＢＰ神经网络的工作包括输入信息的正向传播和误差的反向传播．当处于输入信息图１ＢＰ神经网络拓扑结构图Ｆｉｇ．１ＢＰｎｅｕｒａｌｎｅｔｗｏｒｋｔｏｐｏｌｏｇｙｄｉａｇｒａｍ的正向传播阶段时，输入信息将被逐层处理，且每层神经元的状态仅影响到下一层神经元的状态．当从ＢＰ神经网络的输出结果与预期不一致时，ＢＰ神经网络将进入误差的反向传播阶段，即误差将输出层开始沿原路返回，并按照误差梯度下降的方式逐层修改各层的权值．ＢＰ神经网络的拓扑结构图，如图１所示．在工作期中，ＢＰ神经网络只实现输入信息的正向传播过程，且该过程的计算将以之前确定的各层神经元之间的连接权值为根据进行．因此，学习期的误差反向传播将成为ＢＰ神经网络计算的９６７第５期陈丽枫，等：采用ＨＯＧ特征和机器学习的行人检测方法犺狋狋狆：∥狑狑狑．犺犱狓犫．犺狇狌．犲犱狌．犮狀关键．ＢＰ神经网络一般包括输入层、隐藏层和输出层．输入层神经元的个数取决于训练样本的特征维度，隐藏层神经元的个数根据试验情况而定，输出层神经元的个数为样本的分类个数．由于隐藏层神经元个数的选取没有明确的理论指导，因此，隐藏层神经元个数的选取可以结合前人经验和实验的具体需图２Ａｄａｂｏｏｓｔ算法原理图Ｆｉｇ．２ＳｃｈｅｍａｔｉｃｄｉａｇｒａｍｏｆＡｄａｂｏｏｓｔａｌｇｏｒｉｔｈｍ求来确定．４犃犱犪犫狅狅狊狋?犅犘模型Ａｄａｂｏｏｓｔ算法生成强分类器犎（狓）的过程，","keyWordsShow":"\u003cem\u003e\u003cspan class\u003d\"yellow\"\u003e由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]\u003c/span\u003e，因此在其他的检测\u003c/em\u003e","title":"采用HOG特征和机器学习的行人检测方法","author":"陈丽枫 王佳斌 郑力新","comeFrom":"期刊","percentage":84.0217,"type":"red","periodical":"华侨大学学报：自然科学版","periodicalYear":"2018","vol":"39","beginPage":6.0,"endPage":6.0,"num":"5","organ":"华侨大学工学院","textid":"fcbfc7e193ffbfda93a8c045b3d520f4","category":"CJFD","context":"如ＳＩＦＴ特征，但ＨＯＧ特征能够通过对部分重叠区域进行计算及归一化处理，从而提高精确度［９?１１］．\u003cem\u003e由于它对光照变化、阴影等噪声具有较好的鲁棒性，并且能够产生良好的检测效果，因此\u003c/em\u003e，ＨＯＧ特征成为目前使用较为广泛的一种图像特征．３犅犘神经网络ＢＰ神经网络是一种按照误差逆向","copyCount":2.0,"startIndex":9341.0,"endIndex":9390.0,"checkid":1.33210727E8,"isQueto":0.0,"md5Code":"e34962e5631cdb6d2e264834e84e854f"}],"checkContent":"然不只是行人检测，由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]，因此在其他的检测","endIndex":9390.0,"checkContentShow":"很大的印象，因此这种情况下就适合提取HOG特征作为分类的标准。当\u003cem\u003e然不只是行人检测，由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]，因此在其他的检测\u003c/em\u003e方面也拥有着很大的优势，取得了显著的成果。\n2.3 SVM算法\n","chapterNum":0.0,"index":18},"1356":{"num":2.0,"wordNum":16.0,"startIndex":1356.0,"color":"red","copyRate":71.1016,"yyIndexs":[],"list":[{"id":3.2382757E7,"fileName":"2019052108283143859751.doc","checkContent":"a large number of positive and negative samples are extracted and\u003cbr/\u003ea large \nnumber of positive and negative training samples","returnContent":"y, train \nhuman body model from the training database secondly. The database includes a large \nnumber of positive and negative training samples. So the pedestrian detection speed \nhas been improved. \n★Based on the gradient orientation histogram features (HOG) proposed by Dalal \nand boosted cascade algorithm proposed by Viola, we combine these both of them and \napply them to the pedestrian detection in this paper. In order to reduce the detection \ncomplexity, the detection time and improve detection performance, we have them \ncompleted in several aspects: a) complete the one-step cascade detection algorithm.","keyWordsShow":"\u003cem\u003e The HOG features of a large number of positive and negative samples are extracted and labeled\u003c/em\u003e","title":"基于梯度特征和级联分类的快速行人检测","author":"肖永刚","comeFrom":"博硕","percentage":71.1016,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2010","num":"02","organ":"天津大学","textid":"a5772bcac4c3f438d10d01c0ee0ccaff","category":"CMFD","context":"training database secondly. The database includes \u003cem\u003ea large \nnumber of positive and negative training samples\u003c/em\u003e. So the pedestrian detection speed \nhas been ","copyCount":16.0,"copyKeywords":"行人检测;级联分类器;统计机器学习;支撑向量机;梯度方向直方图; AdaBoost算法","startIndex":1356.0,"endIndex":1450.0,"checkid":1.33210615E8,"isQueto":0.0,"md5Code":"8635938e17550a0a60d8e29eda76cb82"}],"checkContent":" The HOG features of a large number of positive and negative samples are extracted and labeled","endIndex":1450.0,"checkContentShow":"ed based on the Python language.\u003cem\u003e The HOG features of a large number of positive and negative samples are extracted and labeled\u003c/em\u003e. The SVM algorithm is used to t","chapterNum":0.0,"index":2},"9188":{"num":17.0,"wordNum":33.0,"startIndex":9188.0,"color":"red","copyRate":73.2761,"yyIndexs":[],"list":[{"id":3.2382659E7,"fileName":"2019052108283143859751.doc","checkContent":"对于每一个重叠的block块内的细胞单元cell进行了归一化\u003cbr/\u003e对于每一个重叠block\n块内的cell进行对比度\n归一化","returnContent":"所以有些轻微的手势动作可以被忽略，而不会影响对手势的检\n测识别。\n3.2.2 手势图像的 HOG 特征提取\n手势图像的 HOG 特征提取算法主要步骤是：首先，将目标图像作归一化处\n理，采用合适的梯度算子计算手势图像梯度，统计梯度方向直方图并进行归一化，\n最后将直方图串联组合起来就构成了整幅手势图的描述算子。具体算法流程图如\n图 3.3 所示。\n细胞单元\n由细胞单元构\n成的区域块\n带有重叠的区域块\nHOG特征向量： { }1 2, , ,h df \u003dx x (42)x\n检测窗口\n归一化图像\n计算梯度\n对于每一个重叠block\n块内的cell进行对比度\n归一化\n把所有block内的直方\n图向量一起组合成一个\n大的HOG特征向量\n对于每一个cell块对梯\n度直方图进行规定权重\n的投影\n图 3.3 手势 HOG 特征提取流程图\n具体过程如下：\n（1）标准化 Gamma 空间和颜色空间\n为了减少光线变化等因素的干扰，需要对采集到的手势图像先作标准化处理，\n使得手势图像有相同的标准。这样处理后就能够调节亮度比，减少光照等变化所\n带来的影响，同时也可以对噪声干扰产生一定的抑制作用。","keyWordsShow":"\u003cem\u003e对于每一个重叠的block块内的细胞单元cell进行了归一化处理，\u003c/em\u003e","title":"基于PCAHOG与LBP特征融合的静态手势识别方法研究","author":"王瑶","comeFrom":"博硕","percentage":72.6557,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"02","organ":"兰州理工大学","textid":"7845009dd2231edcfcdfa56a06655a3b","category":"CMFD","context":"G特征向量： { }1 2, , ,h df \u003dx x (42)x\n检测窗口\n归一化图像\n计算梯度\n\u003cem\u003e对于每一个重叠block\n块内的cell进行对比度\n归一化\u003c/em\u003e\n把所有block内的直方\n图向量一起组合成一个\n大的HOG特征向量\n对于每一个cell块对","copyCount":1.0,"copyKeywords":"静态手势识别;梯度方向直方图;支持向量机;主成分分析;融合特征;局部二值模式","startIndex":9188.0,"endIndex":9221.0,"checkid":1.33210724E8,"isQueto":0.0,"md5Code":"518e23d766a4cdaf08ce3fcf12a4a9bd"},{"id":3.238266E7,"fileName":"2019052108283143859751.doc","checkContent":"每一个重叠的block块内的细胞单元cell进行了归一化\u003cbr/\u003e每一个重叠的 _\nblock块内的cell进\n行对比度归一化","returnContent":"垂直方向上的梯度算子对单元中的像素点进行计算，统计出梯度或\n边缘方向的直方图，第二步将所有单元内的直方图组合成一个特征描述符。采用\n的梯度算子为：[-1，�，1]跟[-l,�,l]T，梯度方向计算公式如下：\nGx{x,y) \u003dl{x + l,y) -I(x-l,y) (4.5)\nGy(x,y) \u003d Kx,y+ 1) - l{x,y - 1) (4.6)\nGix.y) \u003d ^Gx(x,y)2 + Gyix,y)2 (4.7)\n0(x,y) \u003d arctan(^^j (4.8)\n将单元内计算到的梯度直方图在更大的区域内进行归一化处理，可以通过先\n计算梯度直方图在大区间中的分布密度，再根据这个密度对区域中包含的每一个\n单元做归一化的处理。相比于其他特征，方向梯度直方图特征对关照跟形状变换\n不太敏感，有良好的稳定性。\nHOG特征提取的基本过程如下图所示：\n46\n第四章基于多特征融合的哈希方法\n检测窗口\n■ I ■\n归一化图像\n■ I ■\n计算梯度 .\n \\―■■…―\n对于每一个cell分\n块对梯度直方图进\n行规定权重的投影\n对每一个重叠的 _\nblock块内的cell进\n行对比度归一化\nT\n把所有block内的直方图向量一\n起组合成一个大的HOG特征向量\n图4.","keyWordsShow":"\u003cem\u003e于每一个重叠的block块内的细胞单元cell进行了归一化处理，\u003c/em\u003e","title":"大规模图像检索的哈希算法研究","author":"叶志强","comeFrom":"博硕","percentage":73.2761,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2017","num":"08","organ":"合肥工业大学","textid":"0afd5c6e64da2826c857537388ff1e5f","category":"CMFD","context":" I ■\n计算梯度 .\n \\―■■…―\n对于每一个cell分\n块对梯度直方图进\n行规定权重的投影\n对\u003cem\u003e每一个重叠的 _\nblock块内的cell进\n行对比度归一化\u003c/em\u003e\nT\n把所有block内的直方图向量一\n起组合成一个大的HOG特征向量\n图4.","copyCount":1.0,"copyKeywords":"哈希;图像检索;角度映射;迭代量化;特征融合","startIndex":9189.0,"endIndex":9221.0,"checkid":1.33210724E8,"isQueto":0.0,"md5Code":"518e23d766a4cdaf08ce3fcf12a4a9bd"}],"checkContent":"对于每一个重叠的block块内的细胞单元cell进行了归一化处理，","endIndex":9221.0,"checkContentShow":"HOG特征提取使用场景\n我们可以看出，在HOG特征的生成过程中，\u003cem\u003e对于每一个重叠的block块内的细胞单元cell进行了归一化处理，\u003c/em\u003e因此对于一些局部变化较小的图像使用HOG特征提取是效果最好的，比","chapterNum":0.0,"index":17},"11067":{"num":23.0,"wordNum":52.0,"startIndex":11067.0,"color":"red","copyRate":54.727,"yyIndexs":[{"startIndex":11090.0,"endIndex":11126.0}],"list":[{"id":3.23838E7,"fileName":"2019052108283143859751.doc","checkContent":"NMS算法\n2.4.1 NMS算法介绍\nNMS即是非极大值抑制算法, 对于相交的框, 选择得分最高的, 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所示。图中中间加粗的直线就是分类超平面，而两边的实线就是\n19\n与分类超平面平行且经过距离分类超平面最近的样本点的平面，它们之间的距离\n就是分类间隔。","keyWordsShow":"\u003cem\u003e支持向量机\n支持向量机的算法思想就是寻找线性可分模型的最优分类面，也就是\u003c/em\u003e","title":"基于GASVM的煤矸石混合料抗压强度预测研究","author":"崔曙东","comeFrom":"博硕","percentage":56.4724,"type":"red","periodical":"中国优秀硕士学位论文全文数据库","periodicalYear":"2013","num":"04","organ":"河北工程大学","textid":"7c78ec4478a69da1b24dbcb71d10b81a","category":"CMFD","context":"能会导致学习算法低效，出现过拟合\n现象，推广泛化能力也就减弱了。\n3.1.2 支持向量机的基本概念\n\u003cem\u003e支持向量机是从由两类线性可分的最优分类面发展而来的，所谓的最优分类面\n就是\u003c/em\u003e要求不但将两类待分类的样本数据进行正确的分类，而且要求是分类间隔最\n大[39]，如图 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				<span class="block a_left f12 reportinfo tahoma"> <b class="f14">维普论文检测-大学生版</b><br>
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			<h1 class="f28 block a_center yahei">毛其林_00944_论文定稿</h1>
			<br>南京工程学院<br>毕业设计说明书(论文)<br>作   者：     程旭     学   号：  208150116   <br>学   院：        信息与通信工程学院           <br>专   业：            通信工程                 <br>题   目：  基于深度学习的图像人数检测软件设计 <br>指导者：         毛其林      讲师            <br>评阅者：                                      <br>2019  年  5 月<br>摘  要<br>随着安全意识逐渐深入人心，视频监控已经遍地普及。同时在学校中的考勤工作存在着任务量大、工作难以展开的问题。本论文将这两者结合起来，主<a id="a_detail_0" href="javascript:;" partid='0' startIndex='325' class=red>要阐述了基于深度学习的图像人数检测软件的设计与实现，通过该检测软件，可以将监控图像中的人数进行检测识别统计，</a>从而大大解决考勤工作存在的种种难题，使学校中的考勤工作变得更加自动化、信息化、高效化。<br>本课题是基于Python语言进行开发，对大量的人头正负样本集的HOG特征进行提取并标记，使用SVM算法对得到的特征数据进行训练和学习，得到能够预测出是否是人头的分类器。在进行图像检测时，将检测窗口滑动对图像的局部使用分类器进行逐个检测分类，把检测出是人头的图像的检测窗口坐标记录下来，之后使用NMS算法对检测结果进行优化，去除对于同一个人头重复的检测窗口，根据优化后的检测窗口的数量确定图像中的人数，从而达到图像人数检测统计的目的。<br>关键词：深度学习；HOG特征；SVM算法；NMS算法<br>Abstract<br>As security awareness has gradually penetrated the hearts of the people, video surveillance has become widespread. At the same time, the attendance work in the school has problems of large tasks and difficult work.<a id="a_detail_1" href="javascript:;" partid='1' startIndex='890' class=red> This thesis combines the two, mainly to expound the design and implementation of the image detection software based on deep learning</a>. Through the detection software, the number of people in the surveillance image can be detected and recognized, thus greatly solving the existence of attendance work. All kinds of problems have also made the attendance work in schools more automated, informatized and efficient.<br>This topic is developed based on the Python language.<a id="a_detail_2" href="javascript:;" partid='2' startIndex='1356' class=red> The HOG features of a large number of positive and negative samples are extracted and labeled</a>. The SVM algorithm is used to train and learn the obtained feature data to obtain a classifier that can predict whether it is a human head. <a id="a_detail_3" href="javascript:;" partid='3' startIndex='1591' class=red>In the image detection, the detection window sliding is used to classify the local use classifier of the image</a> one by one, and the detection window coordinates of the image detecting the human head are recorded, and then the detection result is optimized by using the NMS algorithm to remove the same person. The head repeating detection window determines the number of people in the image according to the number of optimized detection windows, thereby achieving the purpose of image number detection and statistics.<br>Keywords: Deep learning; HOG feature; SVM algorithm; NMS algorithm<br>目录<br> 1.1 引言	1<br> 1.2 项目背景与意义	1<br> 1.3 本文的研究内容	2<br> 1.4 本文组织结构	3<br> 第二章  相关技术及开发方式	4<br> 2.1 Python概述	4<br> 2.1.1 Python语言的优点	4<br> 2.1.2 Python语言的缺点	5<br> 2.1.3 使用的Python的第三方库	6<br> 2.2 HOG特征	7<br> 2.2.1 特征提取的简介	7<br> 2.2.2 HOG特征提取原理	8<br> 2.2.3 HOG特征提取使用场景	9<br> 2.3 SVM算法	10<br> 2.3.1 SVM算法介绍	10<br> 2.3.2 核函数	11<br> 2.3.3 SVM中常见核函数	11<br> 2.4 NMS算法	12<br> 2.4.1 NMS算法介绍	12<br> 2.4.2 NMS算法实现步骤	13<br> 2.5本章小结	14<br> 第三章  图像人数检测软件需求分析与设计	15<br> 3.1 可行性需求分析	15<br> 3.2 图像人数检测软件总体设计	15<br> 3.3 设计方案的思考	16<br> 3.3.1 开发语言	16<br> 3.3.2 分类器	17<br> 3.3.3 图像特征	18<br> 3.3.4 机器学习库	18<br> 3.4 样本集	19<br> 3.4.1 样本集采集工具	19<br> 3.4.2 样本集图片的选材	19<br> 3.4.3 样本集图片的数量	20<br> 3.4.4 样本集图片的大小	20<br> 3.5 检测图像	21<br> 3.5.1 检测窗口的大小	21<br> 3.5.2 检测窗口滑动的大小	21<br> 3.5.3 检测窗口的去重	22<br> 3.6 本章小结	22<br> 第四章  图像人数检测软件实现	23<br> 4.1 项目概述	23<br> 4.2 主要功能的实现	24<br> 4.2.1 获取图片信息功能的实现	25<br> 4.2.2 HOG特征提取功能的实现	25<br> 4.2.3 SVM算法训练获取分类器功能的实现	27<br> 4.2.4检测窗口滑动功能的实现	27<br> 4.2.5窗口图像检测功能的实现	28<br> 4.2.6 NMS算法优化功能的实现	28<br> 4.3 性能指标参数的讨论	29<br> 4.3.1 识别率	29<br> 4.3.2 检测速度	29<br> 4.4 本章小结	30<br> 第五章 总结与展望	31<br> 致  谢	32<br> 参考文献	33<br>第一章  绪论<br>1.1 引言<br>机器学习技术已经渗透到了现在社会的方方面面。我们日常使用的智能手机中的相机功能中的检测人脸的位置进行自动聚焦，日常使用的微信等软件可以将语音转换为文字，家庭中出现越来越多的扫地机器人等都离不开机器学习。而随着人们对社会公共安全重视程度的不断提高，智能视频监控系统在人们的日常生活中也发挥着越来越重要的作用。在教育领域，各个学校也都安装了视频监控系统，但是我们对于视频监控系统的利用还仅限于安全方面，不能够充分的发挥它的作用。我们可以利用图像人数检测软件来对监控中的图像进行识别检测，从而对学生的出勤情况进行掌握，以便以学校方面教学方案的调整和优化。<br>图像人数检测软件主要用于学校教室的考勤工作，分为三部分完成。第一部分需要建立大量的人头样本库和非人头样本库，由于机器学习的预测准确率和样本中包含的信息是呈正比的，因此需要制作大量差异性较大的样本数据集来进行训练。第二部分则是需要提取出样本的梯度方向直方图（Histograms of Oriented Gradients，HOG）特征，通<a id="a_detail_4" href="javascript:;" partid='4' startIndex='3578' class=red>过支持向量机(Support Vector Machines，SVM）算法</a>来对这些特征进行训练拟合，形成人头分类器。第三部分对图像进行逐块的检测，利用生成的人头分类器来对块中的图像进行分类，从而达到识别人头的目的。<br>1.2 项目背景与意义<br>课堂是学生们学习科学文化知识和培养正确的人生观价值观的地方，作为学生，我们应该准守学校的几率，按时到达教室参与课堂活动。考勤工作对于培养良好的班级风气，形成严格的教学纪律有着重大的影响作用，同时可以培养学生诚实守时的品格。而传统的考勤工作的不足之处主要有以下三个方面：<br>（1）对于考勤工作人员来说，由于高校中人数很多，而学生上课时安排固定的教室或者固定的座位也不现实，这就导致了高校考勤管理工作量过大，同时效率极其低下等问题。<br>（2）对于老师来说，由于自己上课教学的学生比较多，老师不可能一一认识，而通过传统的点名的方式来进行考勤虽然可以达到期望的效果，但是不仅浪费了宝贵的上课时间，而且也给老师带来了很多的麻烦。<br>（3）对学生而言，由于考勤机制不够完善导致考勤工作难以展开，这就给学生创造了可趁之机，缺勤旷课成为了家常便饭，严重的影响了教学秩序，违背了高等教学的初衷。<br>如何更加信息化、高效化及自动化成为了课堂点名的一大痛点，由此图像人数检测软件应运而生。针对传统考勤方式的不足之处，该图像人数检测软件通过摄像头拍到的监控画面进行检测和识别，将图像中的人数统计出来，从而达到考勤的目的。考勤管理人员可以大大提高工作效率，使管理工作进行的更加完善；老师可以更加地节省课堂时间, 提高课堂教学的效率；学生可以极大的提高出勤率，使教学工作更加顺利的进行。有利于建设严整的校风学风，同时使考勤管理从繁杂的工作中脱离出来，实现自动化、信息化考勤。<br>1.3 本文的研究内容<br>本论文的研究内容是基于深度学习的图像人数检测软件的设计与实现，通过提取大量的正负样本集图片的HOG特征来作为SVM算法的输入，SVM算法通过对大量的HOG特征进行训练和拟合，生成可用于识别是人头或非人头的分类器。当需要检测的图像传来之后，通过窗口滑动来进行对每个窗口进行检测分类，如果是人头就将计数器加一，如果不是人头就继续滑动窗口，直到将整个图像检测结束。此<a id="a_detail_5" href="javascript:;" partid='5' startIndex='4510' class=red>时还需要使用非极大值抑制（Non-Maximum Suppression，NMS）算法来对检</a>测到的人头进行优化，提高准确率。由于样本库对于该软件的检测准确率的影响很大，因此我们在建立样本库的时候需要将每个样本尽可能的差异化，从而使整体的样本集包含的信息更多，同时在尽可能差异化的样本集的基础上尽可能的多，使样本集拟合的更加完整，形成较为准确的分类器。但是样本集的数量也不是越多越好，如果数量过多，可能会出现过拟合的情况，这样对于分类器的准确率是有负面效应的，因此需要多次尝试来找到合适的样本数量来形成最为精确的分类器，使该分类器具有更好的分类效果，即识别效果。<br>1<a id="a_detail_6" href="javascript:;" partid='6' startIndex='4792' class=red>.4 本文组织结构<br>本论文主要分为五个章节，首先在第一章主要介绍了</a>图像人数检测软件的开发背景，课题项目的背景和意义以及该图像人数检测软件需要研究的具体内容。其次在第二章会对图像人数检测软件设计过程中所使用的技术进行特点的描述和原理的讲解。然后第三章则是对图像人数检测软件的可行性和相关需求的讲解。对于第四章，主要讲述了图像人数检测软件的实现人头检测的具体方法和核心代码解释以及测试过程中测试用例的使用等相关信息。最后在第五章主要讲解了本课题过程中的总结和图像人数检测软件存在的不足，同时提出一些解决方案的设想。<br>第二章  相关技术及开发方式<br>图像人数检测软件是基于Python语言进行开发的，使用Python的机器学习库Scikit-learn来简化对样本的HOG特征的提取和使用大量特征进行SVM训练学习的过程。其中使用NMS算法对检测出的人头进行优化处理，防止一个人头被多次检测。图像人数检测软件主要使用Python、HOG特征、SVM算法、NMS算法等技术。<br>2<a id="a_detail_7" href="javascript:;" partid='7' startIndex='5226' class=red>.1 Python概述<k class="yellow"><br>Python是一种简单的、解释型的、交互式的、可移植的、面向对象的高级语言[1]</k>。</a>由于近来几年大数据的火热和人们对于人工智能的追捧，Python语言也由于其简洁易学的语法、对数据便捷的操作深得技术开发者的喜爱。不少人认为Python是一个新兴的语言，但其实并不是。Python是在1994年由Python之父Guido van Rossum正式发布的，可<a id="a_detail_8" href="javascript:;" partid='8' startIndex='5415' class=red>以算的上是编程语言中的老大哥了。<br>2.1.1 Python语言的优点<br>Python语</a>言有以下优点：<br>（1）语法简单易学<br>使代码具有高度的可读性是Python语言的设计初衷之一。为了达到这个目的，Python的语法非常简单，摒弃了C语言中复杂的指针操作和频繁出现的内存泄露问题（使用垃圾回收机制），非常接近于自然语法，代码适合人类阅读。简单易学的语法使Python让我们的开发去更加专注于解决问题而不是专注于语言本身。<br>（2）跨平台特性<br>曾经Java语言刚开发出来的时候依靠跨平台的特性疯狂的抢占C语言和C++语言的市场，由此可见跨平台的重要性。跨平台特性使我们编写的代码可以在任何一种安装了Python环境的平台上都可以直接运行，而不是需要重新编写一份代码，解决了开发者对于不同的平台需要编写不同的代码来进行兼容的问题，实现了真正的可移植性。<br>（3）丰富的第三方库<br>Python内置的标准库已经非常强大了。由于Python语言开源的特性，全球的开发者都可以将自己的Python库进行贡献，这就使得Python的第三方库也更加丰富。这些第三方库可以帮助我们处理各种工作，同时也使得我们解决问题的开发代码变得更加精简。<br>（4）代码优雅规范<br>在其他语言中，一般是使用分号来作为一个语句的结束，使用大括号来表明这是一个代码块，但是在Python中，为了提高代码的可读性，每一行就是一个语句，如果要写另一个语句就需要强制换行，同时采用强制缩进的方式来代替其他语言中的大括号，只要缩进是一样的，那么就属于同一个代码块，这就使得Python的代码看起来极其整齐易读。<br>（5）完<a id="a_detail_9" href="javascript:;" partid='9' startIndex='6096' class=red>全面向对象<br>面向对象和面向过程是编程的两种思维方式。使用面向对象思维</a>写出来的代码之间耦合度更低、内聚性更高，这样系统更加容易扩展，且成本更低。而Python是一种完全面向对象的语言，在Python中一切皆是对象，在面向对象的使用上较其他面向对象的语言也更为灵活。<br>2.1.2 Python语言的缺点<br>当然，Python语言也不是完美的，它也存在这一些缺点，具体如下：<br>（1）运行速度慢<br>Python一直被人诟病的地方就是这个语言写出来的代码运行速度太慢，这也是因为Python的语言的特性导致的，首先Python是一种解释性语言，从源代码到运行出结果经历了读取、词法分析、解析、编译、解释和执行一系列步骤，同时Python又是一种动态语言，这就意味这Python在声明变量的时候类型是可以动态变化的，这就导致Python在比较和转换类型的时候都比较耗时，因为每次与变量相关的操作都要首先进行检查类型。同样完成一个程序，Python写出来的代码执行速度可能会比其他语言例如C++、Java等慢上2-10倍。不过这点速度对于用户来说还是感知不到的，如果系统确实有速度要求的话，可以采用C++来改写关键代码。<br>（2）不易重构<br>我们都知道动态语言的重构是比较困难的，而Python作为动态语言也同样不例外，虽然Python可以使用一些设计模式和重构技巧来完成重构，但是和一些静态语言（如Java）相比，重构还是比较困难的。因为一些错误在编译的时候是不会体现出来的，但是运行的时候就会报错，这也就是动态语言的缺陷之一。<br>2<a id="a_detail_10" href="javascript:;" partid='10' startIndex='6752' class=red>.1.3 使用的Python的第三方库<br>我们使用到的Python第三方函数库如下：<br>（</a>1）NumPy库<br>NumPy（Num<a id="a_detail_11" href="javascript:;" partid='11' startIndex='6813' class=red>erical Python）是Python语言的一个用于操作多维数组和矩阵</a>的第三方扩展程序库。在Python的标准库中虽然也存在Array模块，但是这个模块只能用来操作简单的一维数组，对于多维数组他就无能为力了，矩阵的操作也更不支持，而NumPy程序库的出现就解决了这个问题。它主要用于数组的计算，提供了强大的N维数组对象及其操作的一些方法，同时支持线性代数、傅里叶变换等操作。我们使用这个库主要是需要将图片读出的矩阵进行合并、反转等操作。由于NumPy库是使用C语言编写的，对数组和矩阵的操作不受Python编译器的速度影响，所以用它操作数组和矩阵的效率会远远高于Python代码。当使用的电脑是多核时，使用NumPy库的操作会自动使用并行运算，大大提高操作效率。我们可以在命令行窗口使用pip install numpy来搭建NumPy库的环境。<br>（2）Sciki<a id="a_detail_12" href="javascript:;" partid='12' startIndex='7197' class=red>t-learn库 <k class="yellow"><br>Scikit-learn是Python的一个开源机器学习库，同时是一款简单有效的数据挖掘和数据分析工具[2]</k>。它是专门开发出来做机器学习的，</a>它拥有着可以用来监督学习和无监督学习的方法，但是我们在使用的时候一般是使用它的监督式学习。该库中的大部分函数可以分为估计器和转化器两种。估计器，我们可以理解为分类器，主要用于对数据进行训练和预测。转换器主要用于对数据进行预处理和数据转换，例如标准化、降维等操作。特征提取作为数据挖掘任务重的一个重要环节，往往对于最终结果的影响要高于数据挖掘的算法本身。而在Scikit-learn库的feature_extraction模块也提供了特征提取的功能，使特征提取的操作变得更为方便和易用。同时对于Scikit-learn函数库我们也可以通过继承sklearn.base包下的类进行扩展，例如通过继承BaseEstimator这个估计器的基类来对估计器进行扩展，这就使得Scikit-learn函数库具有更高的自由性，用户可以根据自己的情况进行一些算法的定制，以便于满足自己特殊业务的需求。我们可以在命令行窗口使用pip install scikit-learn来搭建Scik<a id="a_detail_13" href="javascript:;" partid='13' startIndex='7712' class=red>it-learn库的环境。<br>2.2 HOG特征<br>2.2.1 特征提取的简介<br>在视觉的运用和计算机图像的处理方面，</a>特征提取一直起着不可或缺的作用。它是指通过使用计算机来得到图像的信息，从而得到一类事物的特征，然后想要进行检测的时候我们就可以使用需要检测的图像进行特征的匹配，如果匹配成功，则属于该事物，如果匹配不成功，则不属于。特征提取的一个重要的优点在于可以降低事物在真实世界的复杂度，使事物先用特征表示出来，更加便于进行操控。<br>通常我们在提取特征的时候都是寻找一个图像中事物的边缘部分，因为一般在边缘部分两边图像的内容差异才会比较大，这样才能更好的确定物体，而寻找边缘的方法则是计算像素的梯度。梯度，顾名思义，就是一个图像中变化的程度，而在边缘处变化的程度明显是相对比较大的，就如同我们现实生活中的悬崖，悬崖两侧的差异明显变大。因此当我们寻找边缘的时候，我们可以计算大量的梯度来进行寻找。同时我们在进行特征提取的时候也需要将图像进行灰度化，这么做的原因就是图像很容易受到环境的影响，比如光线过亮，那么与正常检测时检测出来的结果一般会有差异，这就会导致我们计算的误差，为了减少这种误差，我们将图像进行灰度化，经过灰度化之后图像的颜色等变化受环境或者光照的影响会明显降低。我们将特征提取得出的结果叫做特征描述或者叫做特征向量。<br>特征提取的方案有多种，其中<a id="a_detail_14" href="javascript:;" partid='14' startIndex='8288' class=red>最为著名的三种图像特征提取方案为方向梯度直方图（HOG）特征、局部二值模式（LBP）特征、Haar特征。HOG特征</a>主要适用于做局部变化对于整体影响变化不大的图像特征的提取，通常与SVM算法相结合使用；LBP特征通常适用于对于图像的局部纹理特征进行提取；而Haar特征应用广泛，常<a id="a_detail_15" href="javascript:;" partid='15' startIndex='8427' class=red>见的应用为人脸识别，通常与AdaBoost算法进行结合使用。<br>2.2.2 HOG特征提取原理<br>HOG特征的提取方式是通过计算图像的梯度方向直方图<k class="yellow">。梯度方向</k></a>直方<a id="a_detail_16" href="javascript:;" partid='16' startIndex='8506' class=red><k class="yellow">图已被证实为在目标识别领域非常有效的特征提取算子[3]</k>。HOG特征提取的主要思想就是一幅图像</a>中的局部图像的特征可以使用梯度来进行很好的描述。<br>我们在实际操作的时候，我们当然不能把一个像素作为局部的图像，我们首先需要把整个图像分为一些小的连通区域，我们将这些连通区域成为细胞单元（cells），我们通过提取细胞单元中每个像素点的特征描述来得到轮廓数据，得到整个细胞单元的特征描述，进而提取到整个图像的HOG特征。但是这样得到的结果一般是不精确的，因为直接从细胞单元得到图像的HOG特征的跨度实在是太大了，很容易受到各种因素的影响，比如光照和阴影等。此时我们需要一个过渡，因此我们需要把一些局部的直方图在更大范围内进行一次归一化，这个更大的范围我们称为块（block），当然做归一化并不是单纯的将直方图进行加起来就可以了，我们还需要算出直方图在块中的密度，根据密度算出各个细胞单元所占的权重，然后根据权重进行一个块中的细胞单元的梯度直方图的归一化。通过归一化之后，能够更好的降低光照和阴影对于特征描述结果的影响，我们通常把这归一化的结果叫做HOG特征描述子。最后我们将检测窗口中所有得到的HOG特征描述子进行组合，就得到了整个图像的HOG特征向量。其生成过程中图2.1所示：<br><div class="imgdiv"><img src="
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kiSBJQksKTT6twADAHirjVVY4KGKAAAAAElFTkSuQmCC" /></div><br>图2.1  HOG特征的生成过程<br>2.2.3 HOG特征提取使用场景<br>我们可以看出，在HOG特征的生成过程中，<a id="a_detail_17" href="javascript:;" partid='17' startIndex='9188' class=red>对于每一个重叠的block块内的细胞单元cell进行了归一化处理，</a>因此对于一些局部变化较小的图像使用HOG特征提取是效果最好的，比如在行人检测方面，想要检测出是不是行人，那么行人的胳膊和腿等等的局部位置的变化并不会对结果产生很大的印象，因此这种情况下就适合提取HOG特征作为分类的标准。当<a id="a_detail_18" href="javascript:;" partid='18' startIndex='9332' class=red><k class="yellow">然不只是行人检测，由于它对光照变化、阴影等噪声具有较好的鲁棒性, 并且能够产生良好的检测效果[4]</k>，因此在其他的检测</a>方面也拥有着很大的优势，取得了显著的成果。<br>2.3 SVM算法<br>2.3.1 SVM算法介绍<br>我们在进行机器训练之前，我们希望想要的类别在所有类别中的得分最高显然是不太可能发生的，但是通过机器训练，机器将图像用分数向量的形式进行输出，每个类别都对应一个分数向量，这样就可以达到我们想要的结果。SVM算法就可以做到，SVM算法也就是支持向量机算法，也被称为间隔最大化算法。<br>我们最常见的各种机器学习的形式，无论是否深入，都是监督式学习。SVM算法也不例外，它是一种具有相关学习算法的监督学习模型。SVM算法最基础的应用就是使用SVM算法进行分类操作。分类的方式是通过寻找最优的分类面。最优分类面就是各类事物的边缘点到这个分类面的距离最大。如图2.2所示，左边的分类面肯定不是最优分类面，而右边的分类面到边缘点的距离明显更大。<br><div class="imgdiv"><img src="
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CCFym/83AGOzSMCvYXodAAAAAElFTkSuQmCC" /></div><br>图2.4  NMS算法流程图<br>	NMS算法的实现流程如下：首先我们对目标边界框列表的置信度得分进行排序，将置信度得分最高的边界框添加到最终输出列表，并将其从边界框列表中删除，然后计算出置信度得分最高的边<a id="a_detail_24" href="javascript:;" partid='24' startIndex='11964' class=red>界框与其他每个边界框的IoU，判断每个边界框的IoU是否大于阈值，如果大于阈值，就把边界框删除，然后再次得到置信度得分最高的边界框添加到最终输出列表重复以上过程，知道边界框列表为空</a>，则完成了NMS算法，此时得到的边界框就是经NMS算法优化后的边界框了。流<a id="a_detail_25" href="javascript:;" partid='25' startIndex='12091' class=red>程如图2.4所示。<br>2.5本章小结<br>本章介绍了图像人数检测软件所需的相关技术基础，首先介绍了</a>Python语言的相关优缺点以及本课题研究中使用到了第三方的Python程序库。然后介绍了特征提取技术以及本课题中使用的HOG特征提取技术的原理和使用场景。<a id="a_detail_26" href="javascript:;" partid='26' startIndex='12215' class=red>然后介绍SVM算法、核函数的概念和SVM算法中常见的核函数。最后</a>，我们通过文字加图解的方式介绍NMS算法以及其实现步骤。只有我们对课题所需使用的技术进行详细的了解，在后期课题的设计和实现中才能更加游刃有余。<br>第三章  图像人数检测软件需求分析与设计<br>3.1 可行性需求分析<br>由于科技的发展，国家对于人工智能、深度学习等领域的重视程度逐渐提高。本次课题是基于深度学习的，具有技术的前瞻性。本次课题设计是图像人数检测软件，设计的目的是解决学校中考勤工作存在的各种问题和痛点，具有巨大的实际意义。此软件使用的是HOG特征提取和SVM算法相结合，HOG特征和SVM算法的组合已经广泛的用于目标检测，被认为是处理目标检测的优秀组合。而使用的Python语言也具有简单易学的优点，同时Python在处理数据上具备这其他语言没有的优势，并且Python中已经有第三方库来对HOG特征提取和SVM算法进行了封装，我们只需要传递请求参数调用方法即可，比如我们选用的Scikit-learn，并且这些Python的第三方库是开源的，意味着有大量的开发者都可以参与，这就成就了Python的这些第三方库的安全性和系统的稳定性。同时我们选取的NMS算法也是经过前人验证过这是适合于目标检测的一个算法。这就使得我们在完成设计的技术选型上没有了不合理的地方。因此本次课题具有很大的研究意义和广阔的研究前景。<br>3.2 图像人数检测软件总体设计<br><div class="imgdiv"><img src="
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class=red>降低了软件运行的性能。C语言没有这些特性，虽然不需要额外的开销，但是也给开发者带来了难题，</a>开发者需要时刻注意内存泄露、数组越界等等问题，这些问题在运行的时候暴露出来，抛出的错误也极其不友好，开发者很难直接从错误中找到产生错误的地方，而只能通过调试进行一步步的查找，这就大大的降低了开发的效率。同时由于C语言是不可移植的，代码不能够重用，我们针对不同的系统平台就需要编写不同的代码来适应不同的平台的需求，增加了开发的难度。<br>Python语言隐藏了很多计算机底层的细节，易学易入门，让开发者只需要关注于功能的实现，而不需要关心内存泄露、垃圾回收等等问题，同时该语言对开发者也相对友好，出现错误的时候提示信息比较完整，能够快速准确的定位错误并改正，而且大部分的常用功能都有简洁的调用方式，大大提高了开发者的开发效率，是小白入门编程的绝佳之选。<br>因此，我们选择使用Python语言来作为我们的开发语言。<br>3.3.2 分类器<br>在<a id="a_detail_30" href="javascript:;" partid='30' startIndex='13786' class=red>机器学习的算法中，用于数据挖掘的分类算法有很多种，有以下常见的分类算法。<br>（1）朴素贝叶斯<k class="yellow"><br>朴素贝叶斯分类是一种十分简单的分类算法，朴素贝叶斯的思想是这样的：对于给出的待分类项，求解在此项出现的条件下各个类别出现的概率，哪个最大，就认为此待分类项属于哪个类别[10]</k>。这个算法</a>只需要给出少量的训练数据进行训练即可，因此该算法适合于模型简单且对性能要求比较高的场景。<br>（2）Logistic回归<br>该<a id="a_detail_31" href="javascript:;" partid='31' startIndex='13984' class=red>算法是通过对历史数据的表现的进行分析，从而对未来结果发生的概率进行预测</a>。其优点显而易见，就是计算代价不搞，实现的原理也非常容易理解，同时在进行计算的时候对于时间和内存等方面的需求也是比较低的。但对于一些数据特征有缺失的情况该算法表现效果就比较差，同时该算法非常容易出现拟合程度不够高从而分类精度太差的问题<br>（3）<a id="a_detail_32" href="javascript:;" partid='32' startIndex='14140' class=red>决策树<br>决策树算法的目标是创建一个模型用来预测样本的目标值，每个节点</a>对应了一个输入属性，其子节点代表父节点的属性的可能取值。可以看出该算法生成的树结构不需要专家来解读，同时很容易转换为规则，但是最大的缺点就是很容易产生过拟合的情况，导致预测效果不好。<br>（4）<a id="a_detail_33" href="javascript:;" partid='33' startIndex='14269' class=red>支持向量机<br>支持向量机的算法思想就是寻找线性可分模型的最优分类面，也就是该分类</a>面到各类别的间隔最大。对<a id="a_detail_34" href="javascript:;" partid='34' startIndex='14320' class=red>于线性不可分的模型，该算法可以将数据向高维空间映射，同时使用核函数来解决映射</a>过程中的维度灾难的问题。同时该算法对于数据产生过拟合的情况有一定的泛化作用，分类效果较好。但是不能直接进行多分类，需要使用间接的方法来做。<br>由于本课题图像人数检测软件的实现不需要进行多分类，只需要分类是不是人头就可以了，同时需要保证更好的分类效果，所以我们选用支持向量机算法来作为分类器解决分类问题。<br>3.3.3 图像特征<br>HOG特征用于目标检测关注的是目标的形状信息分布，使用HOG特征做特征描述子能够对于图像几何和光学的形变具有良好的鲁棒性，局部区域的变化对于整体的影响不大的目标比较适合。事物在提取特征时，事物平移和旋转对图像是有一定的影响的，而HOG特征提取的是梯度向量，梯度向量可以看做是局部图像在方向和大小上的量化，在一定的程度上可以消除平移和旋转图像带来的影响。HOG特征采用块的概念来对一定区域的细胞单元进行梯度向量的归一化，这就进一步泛化了局部特征，提高了光照带来影响的抵抗性。HOG特征和SVM算法结合也是在进行目标检测时常用的方法。<br>Haar特征，也被称为矩形特征，这是一种基于块的特征[11]。一般可以分为三种：两矩形特征、三<a id="a_detail_35" href="javascript:;" partid='35' startIndex='14833' class=red>矩形特征、四矩形特征。这种特征是使用黑白两色的矩形来进行描述的，特征值就是特征中白色矩形像素和减去黑色矩形的像素和。它反映了图像的灰度变化的情况。矩形特征的缺点就是只能对一些</a>比较简单的图像进行描述。<br>因此，对比看来，我们选用HOG特征来作为我们图像的描述信息。<br>3.3<a id="a_detail_36" href="javascript:;" partid='36' startIndex='14967' class=red>.4 机器学习库<br>OpenCV是一个著名的开源计算机视觉函数库，使用C/C++语言编写，在Windows、Linux、Mac等操作系统上都可以运行。</a>其<a id="a_detail_37" href="javascript:;" partid='37' startIndex='15042' class=red>中有两大重要模块，CV模块和ML模块。CV模块主要包含了大量对于图像的处理的算法，而ML是OpenCV的机器学习库，包含了一些基于统计的分类方法等。</a>同时OpenCV具有图像和强大的矩阵运算能力，提供了非常灵活的用户接口，对于Python语言也有良好的支持。但是OpenCV对于HOG特征的提取支持并不是很好，需要我们自己编写核心代码。<br>Scikit-learn是Python的第三方程序库，它是建立在NumPy、SciPy和Matplotlib等优质的科学计算库的基础上开发出来的，能够高效的进行矩阵运算。它可以支持多种机器学习方式，几乎覆盖了机器学习算法中所有的主流算法，可以实现数据预处理、分类、回归、降维和模型选择等常用的机器学习算法[12]，可以扩展至较大的数据规模。其着重点在于易用性，代码质量、协同工作等。同时其对我们本课题中使用的HOG特征提取、支持向量机算法等操作进行了封装和简化，向外暴露一个简单的容易使用的方法供开发者调用，这对于刚接触机器学习的新手来说无疑是最好的选择。<br>因此，我们选用Scikit-learn库作为我们完成机器学习任务的封装函数库。<br>3.4 样本集<br>在图像人数检测软件课程研究过程中，正负样本集对于我们的最终结果的影响是很大的，因此我们需要对样本集的采集和图片情况进行讨论。<br>3.4.1 样本集采集工具<br>在样本集的采集方面，采集工具是非常重要的，这会直接影响到我们采集样本集的效率。通常有两种方法，一种是使用Photoshop手动截图的方法，这种方法的缺点就是使用的是纯人工的方式，采集样本不仅浪费时间，而且截出来的图像想要进行使用通常还需要进行图像大小的转换；另一种方法就是使用截取图像的脚本程序来进行截取，只需要设定好图像的大小，鼠标点击图像的某个地方，就可以截取以鼠标为中心的设定好的大小的图像，这样的效率就会提升很多。因为本次课题的重点在于如何实现人头的检测、识别和计数，所以没有分心去做图像的截取工具脚本，我们选用Photoshop手动截图的方式来完成数据集的采集。<br>3.4.2 样本集图片的选材<br>正负样本集图片的选材是非常重要的，如果选材选的好，正负样本集之间的差异比较大，那么这就说明整个样本集中包含的信息足够多，生成的分类器也会更加精确。对于正样本，毋庸置疑，我们做目标检测想要检测什么目标，就建立对应目标的正样本集，但是要注意正样本之间我们也应该尽量选取差异较大的样本，尽可能是正样本包含的信息足够多。对于负样本，我们样本图片的选取就和根据目标所在的场景相关，也就是说，我们选取负样本的时候尽量选择检测目标可能出现的场景中的事物的样本，而不要选择和检测目标不会出现的乱七八糟的那些场景的图片，这样的负样本是没有意义的。<br>通常情况下，我们样本集中的负<a id="a_detail_38" href="javascript:;" partid='38' startIndex='16195' class=red>样本的数量是要远大于正样本的数量。<br>3.4.3 样本集图片的数量<br>（1）如果正负样本集的数量太小，</a>我们由正负样本集而得到的特征数据不够多，这样我们在形成人头人类器的时候拟合的程度就不够，生成的分类器自然不够精确。识别图像的时候，我们需要对每一个检测窗口使用分类器进行分类，很多是人头的图像我们可能就因此特征不够匹配而识别不出，同时由于分类器是正负样本集进行拟合的结果，不是人头的地方也有可能因为特征相似而被预测为人头，这两种情况都是我们不想见到的。<br>（2）如果正负样本集的数量太大，我们在实际操作的时候需要把样本集的图片的HOG特征全部得到然后形成特征集传入SVM算法，这就会导致我们在形成特征集和SVM算法对特征集进行训练的过程耗费的时间过于漫长。同时虽然SVM算法已经有了一定的方式可以减少过拟合情况造成的影响，但是由于特征过多，难免会产生正负样本集中的某些数据特征相似的问题，检测时遇到类似的图像我们可能就会无法识别成功。<br>但是这也是相对的，我们在样本库的建立的时候既要考虑检测的准确性，又要尽量减少训练耗费的时长，同时避免过拟合的情况，这就需要我们去均衡。当然对于不同的检测目标样本集的数量也不是相同的，这都是需要我们通过大量实验进行测试，从而得出合适的样本集数量。<br>3.4.4 样本集图片的大小<br>对于样本集图片的大小也是需要考虑的因素之一。我们的样本集中的图片尺寸一般都是相同的，我们在检测的时候是需要滑动窗口得到每一个窗口图像，然后通过分类器进行预测，预测的时候就要求我们传入相同数量的特征向量，也就是需要该窗口图像和样本集中的大小一致。实际上在检测的时候可以将窗口图像进行缩小或放大，使窗口图像大小和样本集中图像大小一致，保证两者具有相同数量的特征向量。<br>如果样本集图片过小，则每个样本中包含的像素点就比较少，对应的样本的特征向量就不够多，这就意味着每个样本集图片包含的信息太少，这是我们不愿意看到的，我们希望的是每个图片包含的信息尽量多一些。<br>如果样本集图片过大，图片中包含的信息虽然变多了，但是提取特征的时候需要的时间也就更长了，而且我们在进行检测的时候如果检测窗口没有样本集图片大，就需要将图像进行放大处理，放大的时候图像是容易失真的，失真就意味这信息的丢失，这样我们放弃了提取特征时长换来的更多信息也得不偿失了，因此我们<a id="a_detail_39" href="javascript:;" partid='39' startIndex='17166' class=red>样本集中的图片也不能过大。<br>3.5 检测图像<br>3.5.1 检测窗口的大小<br>在图像进行检测的时候检测窗口</a>的大小也是我们需要控制的因素，这也是会直接影响我们最终的结果。如果检测窗口过大，一个窗口中可能包含进两个人头，而我们的分类器只能分类出是不是人头，不能判断人头的数量，所以我们应该尽量确保一个窗口只包含进一个人头，再根据分类器的结果，判断出是人头的窗口，再进行优化，根据最终窗口的数量确定人头的数量，从而实现计数。反之，如果检测窗口过小。一个窗口中可能承载不下一个人头，那么这也会影响我们的判断。我们虽然可以通过一些方法来调整图片的大小，但是我们应尽量使调整的幅度变小，从而使丢失的信息更少。<br>3.5.2 检测窗口滑动的大小<br>在进行检测的时候，可能会出现一个人头被两个窗口分隔开的情况，那么如果两个窗口都检测不出是人头的话，我们的结果就会存在误差，而且这种情况是比较容易出现的，所以我们应该避免这种情况。我们应该将检测窗口滑动的步长减小，使步长一定小于检测窗口的大小，这样尽量减少一个人头图像被多个窗口分隔开的情况。<br>3.5.3 检测窗口的去重<br>在进行窗口滑动检测的过程中，我们的水平和垂直滑动的步长是小于检测窗口的宽和高的，因此是可能出现同一个人头被多个检测窗口检测为人头的，由于人头计数的结果是通过检测为人头的检测窗口的个数来决定的，我们如果不进行去重的话，一个人头在计数的时候就会被记成多个，这就会对我们的人头计算的结果的精确度产生很大的影响。因此我们对于重复的检测窗口是需要进行去重操作的。在机器学习领域比较有名的去重算法就是NMS算法，它可以根据重复的检测窗口矩形框列表和对应的置信度分数列表来根据设定的阈值得到适合留下做计数的矩形框列表，从而使图像人数检测软件的准确率得到提高。<br>3.6 本章小结<br>本章主要讲解了图像人数检测软件的需求分析与设计，首先对图像人数检测软件的可行性进行了分析，其次对软件的总体设计进行了概述，然后讲解了在设计过程中设计方案的思考，对于分类效果，样本集方面使我们需要考虑的，我们对其选材、大小、数量进行了讨论，对于检测效果，检测图像时的选择显得极为重要，我们又对检测窗口的大小和窗口滑动的步长进行了讨论和分析。对软件的需求进行分析和设计，是我们之后进行实际实施的时候的基石和指引。<br>第四章  图像人数检测软件实现<br>4.1 项目概述<br>图像人数检测软件程序采用Python语言编写，Python的版本号为3.6，项目在PyCharm中创建项目工程。首先我们需要先搭建PyCharm软件的开发环境，之后需要安装项目中使用到的第三方函数库，例如sklearn、numpy、nms等。根据需求分析与设计阶段的构思，构建项目工程结构如图4.1所示。<br>图4.1 项目工程结构<br>项目目录介绍如下：<br>（1）model包，生成的分类器文件会保存到这个包下的svm.model文件中，如果项目中原来没有这个目录和文件，在运行程序的时候会先创建，然后将分类器保存。<br>（2）negdata包，这里主要保存了项目在进行提取HOG特征的时候使用的负样本的图片。负样本集如图4.2所示。<br>（3）posdata包，这里主要保存了项目在进行提取HOG特征的时候使用的正样本的图片。正样本集如图4.3所示。<br>（4）common_util.py文件，这是项目中的通用的工具文件，该文件中包含了根据路径创建文件文件夹，将单个图片进行灰度化提取HOG特征的方法。<br>（5）get_data.py文件，该文件是包含了根据路径得到图片列表，并对图片进行标记的方法。<br>（6）get_svm_model.py文件，该文件包含了提取HOG特征，并通过SVM算法得到分类器的方法。<br><div class="imgdiv"><img src="
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RS0VRB//2Q==" /></div><br>图4.3 正样本集<br>（7）test_classifier.py文件，该文件用于检测图像，包含了窗口滑动、根据分类器进行预测分类、使用NMS算法对得到的人头矩形框进行优化等等操作。<br>4.2 主要功能的实现<br>各功能主要分为获取图片信息功能、HOG特征提取功能、SVM算法训练获取分类器功能、检测窗口滑动功能、窗口图像检测功能、NMS算法优化功能。<br>4.2.1 获取图片信息功能的实现<br>该功能主要就是通<a id="a_detail_40" href="javascript:;" partid='40' startIndex='19092' class=red>过访问路径下的正样本和负样本图片，并对正负样本进行标记（正样本标记为1，负样本标记为-1）。获取图片</a>信息功能代码的关键代码如下：<br>def get_image_list(folder_name, flag):<br>    image_list = []<br>    label_list = []<br>    path_name = os.path.join(folder_name, '*')<br>    file_name_list = glob.glob(path_name)<br>    for i in range(0, len(file_name_list)):<br>        image = file_name_list[i]<br>        src = cv.imread(image)<br>        image_list.append(src)<br>        if flag == 1:<br>            label_list.append(1)<br>        else:<br>            label_list.append(-1)<br>    return image_list, label_list<br>将正负样本图片获取并标记的功能封装成一个方法，如果是正样本则传入正样本的目录，并传入flag=1，如果是负样本则传入负样本的目录，并传入flag。调用代码如下：<br># 获取正样本相关信息<br>pos_image_list, pos_label_list = get_image_list(pos_train_image_path, 1)<br># 获取负样本相关信息<br>neg_image_list, neg_label_list = get_image_list(neg_train_image_path, -1)<br>4.2.2 HOG特征提取功能的实现<br>该功能主要就是先通过reshape方法将图片矩阵转换需要传入函数的类型，再通过rgb2gray方法进行灰度化处理，最后通过hog方法得到HOG特征。HOG特征提取功能实现关键代码如下：<br>def get_feature(image_list, image_size):<br>    features = []<br>    for i in range(0, len(image_list)):<br>       image = np.reshape(image_list[i], (image_size[0], image_size[1], 3))<br>       gray = color.rgb2gray(image)<br>       hog_feature = hog(image=gray,orientations=9,pixels_per_cell=[2, 2],cells_per_block=[2, 2], visualize=False)<br>       features.append(hog_feature)<br>    return features<br>这里最重要的就是这个hog方法，这个方法是从Scikit-learn程序库中的skimage包下feature模块引入的，这个方法的参数说明如下：<br>image：该参数表示输入的图片<br>orientations：表示所要使用的梯度的方向个数<br>pixels_per_<a id="a_detail_41" href="javascript:;" partid='41' startIndex='20478' class=red>cell：表示每个细胞单元中的像素<br>cells_per_block：表示每个块中的细胞单元<br>visualize：表示</a>是否输出梯度直方图的图像表示<br>我们采用的样本集是20像素*20像素的图片，<a id="a_detail_42" href="javascript:;" partid='42' startIndex='20573' class=red>每个细胞单元采用2像素*2像素大小，每个块采用8像素*8像素大小，也就是每个块包含了2*2大小的细胞单元。<br>将</a>正负样本图片的路径传入方法，得到正负样本的HOG特征。实现代码如下：<br># 得到正样本的特征<br>pos_features = get_feature(pos_image_list, size)<br># 得到负样本的特征<br>neg_features = get_feature(neg_image_list, size)<br>我们在进行将HOG特征和正负样本的标记传入SVM算法中的时候需要传入正负样本集的HOG特征和标记，因此我<a id="a_detail_43" href="javascript:;" partid='43' startIndex='20835' class=red>们需要将得到的正样本的HOG特征和负样本的HOG特征进行合并，同理，正样本的标记和负样本的标记也需要进行合并。</a>关键代码入下：<br># 正负样本特征合并<br>hog_feature = np.concatenate((pos_features, neg_features))<br># 正负样本标记合并<br>hog_label = np.concatenate((pos_label_list, neg_label_list))<br>4.2.3 SVM算法训练获取分类器功能的实现<br>该功能需要将合并后的特征和标记传入SVM算法中，从而得到分类器，并将得到的分类器导出到指定的文件中。SVM算法训练获取分类器功能代码实现如下：<br># SVM 算法训练<br>def svm_train(features, labels):<br>    clf = SVC(kernel='linear',decision_function_shape='ovo',max_iter=100000)<br>    classifier.fit(features, labels)<br>    create_dir(os.path.split(model_path)[0])<br>    joblib.dump(classifier, model_path)<br>我们在SVM算法中使用SVC()来创建一个拥有核函数的SVM，其中kernel=linear表示使用的核函数为线性核函数，decision_function_shape=ovo表示使用一对一分裂，即二分分类，max_iter=100000表示训练过程中最大的迭代次数为100000次，使用fit()方法将传入的特征和标记进行训练，使用create_dir()方法是创建指定的文件来存储导出的分类器，使用dump()方法来将分类器导出到指定文件中。<br><div class="imgdiv"><img src="
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WVQAAAAASUVORK5CYII=" /></div><br>图4.2 SVM训练后目录结构<br>在SVM算法训练之后，生成svm.model文件用于存放分类器。项目的目录结果如图4.2所示。<br>4.2.4检测窗口滑动功能的实现<br>该功能主要是通过对检测图像的矩形表示来进行截取得到每一个窗口，先通过移动横向的步长来滑动，横向滑动到图片最右端之后，再移动纵向的步长，在这新的横向上继续滑动，以此类推。检测窗口滑动功能的关键代码如下：<br>def sliding_window(image, window_size, step):<br>    for start_y in range(0, image.shape[0], step[1]):<br>        for start_x in range(0, image.shape[1], step[0]):<br>            yield (start_x, start_y, image[start_y:start_y + window_size[1], start_x:start_x + window_size[0])<br>我们在实现的时候选用的每个检测窗口的大小为30像素*30像素，横向和纵向移动的步长都是10像素。其中yield()是一个生成器，将遍历产生的数据逐个返回。<br>4.2.5窗口图像检测功能的实现<br>该功能主要是将窗口图像进行灰度转化后然后进行统一化大小，使之与样本集的图像具有相同的特征向量，然后将统一化后的图片进行提取HOG特征，加载分类器进行预测。窗口图像检测功能的实现关键代码如下：<br>gray = color.rgb2gray(im_window)<br>gray = cv2.resize(gray, (20, 20))<br>fd = hog(gray, 9, pixels_per_cell=[2, 2], cells_per_block=[2, 2], visualize=False)<br>fd = np.array(fd).reshape(1, -1)<br>clf = joblib.load(model_path)<br>predict_result = clf.predict(fd)<br>使用joblib.load()方法来根据文件的路径加载分类器，然后使用分类器的predict()方法来对得到的HOG特征进行预测。由于我们标记为1的是人头正样本，其他标记为负样本，所以我们经过预测得到的结果predict_result如果等于1，那么这个检测窗口就是人头。如果预测得到其他的结果，则不是人头。如果检测窗口检测出是人头，我们则可以通过分类器的decision_function()方法来得到该窗口对应的置信度分数。<br>4.2.6 NMS算法优化功能的实现<br>该功能主要是将检测出来是人头的矩形框进行优化，留下置信度分数最高的矩形框，这样就可以根据矩形框的个数来确定人头的个数。NMS算法优化功能实现关键代码如下：<br>best_indexes = nms.boxes(rectangles, scores, nms_threshold=threshold)<br>print(len(best_indexes))<br>使用nms.boxes()方法将矩形框列表、对应的置信度分数列表、阈值传进去，就可以得到留下的矩形框的索引，索引的数量就是留下的矩形框的数量，也就是人头的数量。其中阈值threshold我们使用的是0.4。<br>4.3 性能指标参数的讨论<br>4.3.1 识别率<br>识别率指的是检测出的人数与图像中实际人数的比值。软件在对图像进行检测识别时，图片的清晰度、人像的大小、人的遮挡、训练的样本集等都会影响图像的识别率。<br>如果图片清晰度不够，则局部的图像就不够清晰，包含的信息就比较少，提取到的HOG特征向量就不够完整，那么分类器就可能会产生误判。<br>由于摄像头处于教室中的固定位置，截取出来的图像中的人像就会存在焦距不同的情况。如果焦距过近，图像中的人头相对较大，则可能会多个检测窗口瓜分了一个人头，我们的检测结果就可能会比实际值要多。如果焦距过远，图像中的人头则相对较小，则可能在一个检测窗口中包含多个人头，这样得到的结果就会比实际值少。<br>在图像中由于角度问题也可能会出现人的遮挡的问题，这样被遮挡的人像就无法识别出来，这样会使我们识别出的人数少于实际的人数。<br>样本集的质量对识别率的影响也是巨大的，如果样本集不合适，则训练出的分类器就会存在误差，识别效果就会大大降低，因此我们应该选取合适的样本集。其中最重要的就是负样本，因为我们的图像人数检测软件需要使用在多个教室，因此我们需要对多个教室进行取样来制作负样本，而正样本只需要人头样本和一些带有人脸的样本即可。<br>4.3.2 检测速度<br>我们在对图像进行检测的时候，检测速度也是我们应该考虑的因素。检测时的图像如果过大，进行检测时需要的检测窗口就会增多，由于每个检测窗口都需时间来进行计算检测，所以整个图片的检测速度就会降低，我们可以考虑将图片进行缩小或者将图片进行分割后再检测，但显然也是会存在误差的。<br>图像的检测一般是使用CPU进行的，但是CPU的速度毕竟是有限的。在计算机中，GPU的计算速度是要优于CPU的，我们可以考虑将图像使用GPU来进行检测，这样可以提高我们识别时的检测速度。<br>我们在进行检测的时候，一般情况下只有一张图像在进行检测，我们可以采用多个线程的进行并行计算多个图像，这样我们在相同的时间内可以检测多个图片，也相当于提高了检测的效率。同时我们也可以通过将一个图像进行分割放在多个线程中并行计算来完成图像的检测。<br>4.4 本章小结<br>本章主要是对图像人数检测软件的实现进行了分析，首先介绍了项目的概述，然后对项目中获取图片信息功能、HOG特征提取功能、SVM算法训练获取分类器功能、检测窗口滑动功能、窗口图像检测功能、NMS算法优化功能等功能的实现进行了讲解，讲解的同时，对于实现过程中使用到的函数的功能进行了讲解，对函数的参数也进行了介绍，同时讲解了本软件中对于参数的使用的情况。<br>第五章 总结与展望<br>图像人数检测软件是一个用来减轻学校考勤工作量的一个解决方案，很多学校和机构也对这样类似的考勤软件开展了广泛而深入的开发与研究，本论文希望能为以后参与研发的研究人员提供一些指引和方向。经过大四一个学期的工作，基于深度学习的图像人数检测软件已经初步完成，现将其相关工作总结如下：<br>（1）结合课题的研究背景，分析了图像人数检测软件的需求与实现方案，同时对于软件开发过程中使用到的技术做了学习与了解。<br>（2）对于软件开发过程中使用到的样本集的问题也通过手动创建的方式进行了初步的解决。<br>（3）对于机器学习方面有了一定的了解，对于课题中使用的机器学习相关的算法有了初步的了解，同时也能够对一些常用的方法进行封装和使用。<br>当然软件在目前还存在这一些不足之处有待改进，具体有以下几个方面：<br>（1）样本集的建立不够合适导致软件的检测识别率太低，主要是对于不是人头的地方总是会误判为人头，这是因为软件中负样本的数量过少，可以多增加一些负样本来提高识别率。<br>（2）图像在检测过程中耗费的时间比较长。由于Python语言的特性，加上我们的算法没有进行优化，同时图像也比较大，而我们的样本集图片相对比较小，这样我们检测时需要分出的滑动窗口就比较多，大大的拖慢了检测的时长。我们可以采用GPU进行运算，或者进行多个图片并行计算的方式来提高图像的检测速度。<br>（3）我们在获取样本集的时候，采用的是用PhotoShop手动去对图片进行截图保存以获取样本图片，不过这样非常费时费力，我们可以通过编写一个点击就可以截图并自动保存的程序来提高获取样本集的效率。<br>（4）在项目中的各种路径和配置都是写在文件中的，不够集中，不利于我们进行管理，可以将所有需要配置的地方都放在一个配置文件里，然后通过解析这个配置文件来进行使用，这样我们维护起来也比较方便。<br>致  谢<br>本课题研究历时一个学期，在四个月的软件学习与开发过程中我遇到了很多的问题。很多问题的解决与突破都是在老师与同学的帮助下完成的。我尤其要感谢我的导师毛其林老师。在软件开发过程中，导师给予了我极大的鼓励与帮助，其中包括方向性与技术性的指导。撰写论文时，毛老师耐心地为我解释论文撰写要求与规范，同时针对在论文撰写过程中存在的问题提出了宝贵的建议。从毛老师身上我还学到了学习以外的东西，包括做人、做事的原则和方法，在此，我要对毛老师的指导和教诲表示衷心的感谢。<br>在四年的学习和生活中，我要感谢信息通信工程学院领导和老师的关怀，以及学校为我提供了良好学习的平台，感谢学校图书馆和数据库提供了良好的学习条件和丰富的学习资料。<br>此外，本<a id="a_detail_44" href="javascript:;" partid='44' startIndex='25244' class=red>文中引用了多位学者的论文内容，对我起到了很大的帮助，没有他们的研究成果，我</a>完成本次论文的难度将大大增加。<br>最后，对在整<a id="a_detail_45" href="javascript:;" partid='45' startIndex='25303' class=red>个课题进行过程中所有帮助过我的领导、老师、同学等表示我最真诚的谢意！<br>同时由衷地感谢各位答辩老师能够抽出宝贵时间对我论文的内容进行评阅。由于我的学术水平有限，论文中难免有疏漏和不足之处，恳请各位老师给予批评和指正</a>！<br>参考文献<br>[1] 佚名. Python学习手册[M]. 2011.<br>[2] 杨忆,李建国,葛方振.基于Scikit-Learn的垃圾短信过滤方法实证研究[J].淮北师范大学学报(自然科学版),2016,37(04):39-41. <br>[3] 慕春雷.基于HOG特征的人脸识别系统研究[D].电子科技大学,2013.DOI:"".<br>[4]陈丽枫,王佳斌,郑力新.采用HOG特征和机器学习的行人检测方法[J].华侨大学学报(自然科学版),2018,39(05):768-773.<br>[5]梁礼明,陈明理,刘博文,吴健.基于图论的支持向量机核函数选择[J].计算机工程与设计,2019(05):1316-1321.<br>[6]沈培,张吉凯,张子刚.基于支持向量机的单病种医疗费用控制研究[J].中国卫生经济,2012,31(03):89-91.<br>[7]王燕星. 结构型纹理背景工业产品图像缺陷检测研究[D].北京交通大学,2018.<br>[8]邢延超,程雷雷,李瑞,张化迪.基于MSER和NMS的变形文档字符检测[J].科学技术创新,2018(32):101-102.<br>[9]盛恒,黄铭,杨晶晶.基于Faster R-CNN和IoU优化的实验室人数统计与管理系统[J/OL].计算机应用:1-7[2019-05-13].http://kns.cnki.net/kcms/detail/51.1307.TP.20190129.1009.018.html.<br>[10]查丰. 引力聚类及其应用研究[D].安徽大学,2011.<br>[11]张涛,陈万培,乔延婷,陈舒涵.一种Haar特征车辆检测速度的提升方法[J].无线电工程,2019,49(05):393-396.<br>[12]潘兴广,牛志忠,张明贵.基于Scikit-learn的支持向量回归分析[J].现代信息科技,2019,3(06):9-11.<br><br>
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